
import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		from transformers.models.electra.modeling_flax_electra import (
		    FlaxElectraForCausalLM,
		    FlaxElectraForMaskedLM,
		    FlaxElectraForMultipleChoice,
		    FlaxElectraForPreTraining,
		    FlaxElectraForQuestionAnswering,
		    FlaxElectraForSequenceClassification,
		    FlaxElectraForTokenClassification,
		    FlaxElectraModel,
		)



class       a_   ( unittest.TestCase						):






			def __init__(			self							, __UpperCamelCase							, __UpperCamelCase=13							, __UpperCamelCase=7							, __UpperCamelCase=True							, __UpperCamelCase=True							, __UpperCamelCase=True							, __UpperCamelCase=True							, __UpperCamelCase=99							, __UpperCamelCase=24							, __UpperCamelCase=32							, __UpperCamelCase=5							, __UpperCamelCase=4							, __UpperCamelCase=37							, __UpperCamelCase="gelu"							, __UpperCamelCase=0.1							, __UpperCamelCase=0.1							, __UpperCamelCase=512							, __UpperCamelCase=16							, __UpperCamelCase=2							, __UpperCamelCase=0.02							, __UpperCamelCase=4							, ):
									_lowercase									=				parent
									_lowercase									=				batch_size
									_lowercase									=				seq_length
									_lowercase									=				is_training
									_lowercase									=				use_attention_mask
									_lowercase									=				use_token_type_ids
									_lowercase									=				use_labels
									_lowercase									=				vocab_size
									_lowercase									=				hidden_size
									_lowercase									=				embedding_size
									_lowercase									=				num_hidden_layers
									_lowercase									=				num_attention_heads
									_lowercase									=				intermediate_size
									_lowercase									=				hidden_act
									_lowercase									=				hidden_dropout_prob
									_lowercase									=				attention_probs_dropout_prob
									_lowercase									=				max_position_embeddings
									_lowercase									=				type_vocab_size
									_lowercase									=				type_sequence_label_size
									_lowercase									=				initializer_range
									_lowercase									=				num_choices






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size     )

									_lowercase									=				None
									if self.use_attention_mask:
															_lowercase									=				random_attention_mask([self.batch_size, self.seq_length]     )

									_lowercase									=				None
									if self.use_token_type_ids:
															_lowercase									=				ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size     )

									_lowercase									=				ElectraConfig(
									    vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, embedding_size=self.embedding_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, initializer_range=self.initializer_range							, )

									return config, input_ids, token_type_ids, attention_mask






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.prepare_config_and_inputs()
									_lowercase					,	_lowercase					,	_lowercase					,	_lowercase									=				config_and_inputs
									_lowercase									=				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict







@require_flax
class       a_   ( _a				, unittest.TestCase						):
			a  : Union[str, Any]				      =       True

			a  : Tuple				      =       (
			    (
			        FlaxElectraModel,
			        FlaxElectraForCausalLM,
			        FlaxElectraForMaskedLM,
			        FlaxElectraForPreTraining,
			        FlaxElectraForTokenClassification,
			        FlaxElectraForQuestionAnswering,
			        FlaxElectraForMultipleChoice,
			        FlaxElectraForSequenceClassification,
			    )
			    if is_flax_available()
			    else ()
			)






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				FlaxElectraModelTester(self     )






			@slow
			def       UpperCamelCase_							(			self     ):
									for model_class_name in self.all_model_classes:
															if model_class_name == FlaxElectraForMaskedLM:
																					_lowercase									=				model_class_name.from_pretrained("""google/electra-small-generator"""     )
															else:
																					_lowercase									=				model_class_name.from_pretrained("""google/electra-small-discriminator"""     )
															_lowercase									=				model(np.ones((1, 1)     )     )
															self.assertIsNotNone(__UpperCamelCase     )

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class       a_   ( _a				, unittest.TestCase						):
			a  : List[str]				      =       UnCLIPPipeline
			a  : Optional[int]				      =       TEXT_TO_IMAGE_PARAMS - {
			    '''negative_prompt''',
			    '''height''',
			    '''width''',
			    '''negative_prompt_embeds''',
			    '''guidance_scale''',
			    '''prompt_embeds''',
			    '''cross_attention_kwargs''',
			}
			a  : Optional[int]				      =       TEXT_TO_IMAGE_BATCH_PARAMS
			a  : List[Any]				      =       [
			    '''generator''',
			    '''return_dict''',
			    '''prior_num_inference_steps''',
			    '''decoder_num_inference_steps''',
			    '''super_res_num_inference_steps''',
			]
			a  : Dict				      =       False






			@property
			def       UpperCamelCase_							(			self     ):
									return 32






			@property
			def       UpperCamelCase_							(			self     ):
									return 32






			@property
			def       UpperCamelCase_							(			self     ):
									return self.time_input_dim






			@property
			def       UpperCamelCase_							(			self     ):
									return self.time_input_dim * 4






			@property
			def       UpperCamelCase_							(			self     ):
									return 100






			@property
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
									return tokenizer






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )
									_lowercase									=				CLIPTextConfig(
									    bos_token_id=0							, eos_token_id=2							, hidden_size=self.text_embedder_hidden_size							, projection_dim=self.text_embedder_hidden_size							, intermediate_size=37							, layer_norm_eps=1E-05							, num_attention_heads=4							, num_hidden_layers=5							, pad_token_id=1							, vocab_size=1_000							, )
									return CLIPTextModelWithProjection(__UpperCamelCase     )






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				{
									    """num_attention_heads""": 2,
									    """attention_head_dim""": 12,
									    """embedding_dim""": self.text_embedder_hidden_size,
									    """num_layers""": 1,
									}

									_lowercase									=				PriorTransformer(**__UpperCamelCase     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				{
									    """clip_embeddings_dim""": self.text_embedder_hidden_size,
									    """time_embed_dim""": self.time_embed_dim,
									    """cross_attention_dim""": self.cross_attention_dim,
									}

									_lowercase									=				UnCLIPTextProjModel(**__UpperCamelCase     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				{
									    """sample_size""": 32,
									    # RGB in channels
									    """in_channels""": 3,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 6,
									    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
									    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
									    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
									    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    """layers_per_block""": 1,
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": """identity""",
									}

									_lowercase									=				UNetaDConditionModel(**__UpperCamelCase     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									return {
									    "sample_size": 64,
									    "layers_per_block": 1,
									    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
									    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
									    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    "in_channels": 6,
									    "out_channels": 3,
									}






			@property
			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )

									_lowercase									=				UNetaDModel(**self.dummy_super_res_kwargs     )
									return model






			@property
			def       UpperCamelCase_							(			self     ):
									# seeded differently to get different unet than `self.dummy_super_res_first`
									torch.manual_seed(1     )

									_lowercase									=				UNetaDModel(**self.dummy_super_res_kwargs     )
									return model






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.dummy_prior
									_lowercase									=				self.dummy_decoder
									_lowercase									=				self.dummy_text_proj
									_lowercase									=				self.dummy_text_encoder
									_lowercase									=				self.dummy_tokenizer
									_lowercase									=				self.dummy_super_res_first
									_lowercase									=				self.dummy_super_res_last

									_lowercase									=				UnCLIPScheduler(
									    variance_type="""fixed_small_log"""							, prediction_type="""sample"""							, num_train_timesteps=1_000							, clip_sample_range=5.0							, )

									_lowercase									=				UnCLIPScheduler(
									    variance_type="""learned_range"""							, prediction_type="""epsilon"""							, num_train_timesteps=1_000							, )

									_lowercase									=				UnCLIPScheduler(
									    variance_type="""fixed_small_log"""							, prediction_type="""epsilon"""							, num_train_timesteps=1_000							, )

									_lowercase									=				{
									    """prior""": prior,
									    """decoder""": decoder,
									    """text_proj""": text_proj,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """super_res_first""": super_res_first,
									    """super_res_last""": super_res_last,
									    """prior_scheduler""": prior_scheduler,
									    """decoder_scheduler""": decoder_scheduler,
									    """super_res_scheduler""": super_res_scheduler,
									}

									return components






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase=0     ):
									if str(__UpperCamelCase     ).startswith("""mps"""     ):
															_lowercase									=				torch.manual_seed(__UpperCamelCase     )
									else:
															_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )
									_lowercase									=				{
									    """prompt""": """horse""",
									    """generator""": generator,
									    """prior_num_inference_steps""": 2,
									    """decoder_num_inference_steps""": 2,
									    """super_res_num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}
									return inputs






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				"""cpu"""

									_lowercase									=				self.get_dummy_components()

									_lowercase									=				self.pipeline_class(**__UpperCamelCase     )
									_lowercase									=				pipe.to(__UpperCamelCase     )

									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				pipe(**self.get_dummy_inputs(__UpperCamelCase     )     )
									_lowercase									=				output.images

									_lowercase									=				pipe(
									    **self.get_dummy_inputs(__UpperCamelCase     )							, return_dict=__UpperCamelCase							, )[0]

									_lowercase									=				image[0, -3:, -3:, -1]
									_lowercase									=				image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									_lowercase									=				np.array(
									    [
									        0.99_97,
									        0.99_88,
									        0.00_28,
									        0.99_97,
									        0.99_84,
									        0.99_65,
									        0.00_29,
									        0.99_86,
									        0.00_25,
									    ]     )

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				torch.device("""cpu"""     )



									class       a_   :
												a  : int				      =       1







									_lowercase									=				self.get_dummy_components()

									_lowercase									=				self.pipeline_class(**__UpperCamelCase     )
									_lowercase									=				pipe.to(__UpperCamelCase     )

									_lowercase									=				components["""prior"""]
									_lowercase									=				components["""decoder"""]
									_lowercase									=				components["""super_res_first"""]
									_lowercase									=				components["""tokenizer"""]
									_lowercase									=				components["""text_encoder"""]

									_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )
									_lowercase									=				prior.dtype
									_lowercase									=				1

									_lowercase									=				(batch_size, prior.config.embedding_dim)
									_lowercase									=				pipe.prepare_latents(
									    __UpperCamelCase							, dtype=__UpperCamelCase							, device=__UpperCamelCase							, generator=__UpperCamelCase							, latents=__UpperCamelCase							, scheduler=DummyScheduler()     )
									_lowercase									=				(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
									_lowercase									=				pipe.prepare_latents(
									    __UpperCamelCase							, dtype=__UpperCamelCase							, device=__UpperCamelCase							, generator=__UpperCamelCase							, latents=__UpperCamelCase							, scheduler=DummyScheduler()     )

									_lowercase									=				(
									    batch_size,
									    super_res_first.config.in_channels // 2,
									    super_res_first.config.sample_size,
									    super_res_first.config.sample_size,
									)
									_lowercase									=				pipe.prepare_latents(
									    __UpperCamelCase							, dtype=__UpperCamelCase							, device=__UpperCamelCase							, generator=__UpperCamelCase							, latents=__UpperCamelCase							, scheduler=DummyScheduler()     )

									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				"""this is a prompt example"""

									_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )
									_lowercase									=				pipe(
									    [prompt]							, generator=__UpperCamelCase							, prior_num_inference_steps=2							, decoder_num_inference_steps=2							, super_res_num_inference_steps=2							, prior_latents=__UpperCamelCase							, decoder_latents=__UpperCamelCase							, super_res_latents=__UpperCamelCase							, output_type="""np"""							, )
									_lowercase									=				output.images

									_lowercase									=				tokenizer(
									    __UpperCamelCase							, padding="""max_length"""							, max_length=tokenizer.model_max_length							, return_tensors="""pt"""							, )
									_lowercase									=				text_encoder(text_inputs.input_ids     )
									_lowercase									=				text_inputs.attention_mask

									_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )
									_lowercase									=				pipe(
									    generator=__UpperCamelCase							, prior_num_inference_steps=2							, decoder_num_inference_steps=2							, super_res_num_inference_steps=2							, prior_latents=__UpperCamelCase							, decoder_latents=__UpperCamelCase							, super_res_latents=__UpperCamelCase							, text_model_output=__UpperCamelCase							, text_attention_mask=__UpperCamelCase							, output_type="""np"""							, )[0]

									# make sure passing text embeddings manually is identical
									assert np.abs(image - image_from_text     ).max() < 1E-4






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				torch_device == """cpu"""

									self._test_attention_slicing_forward_pass(test_max_difference=__UpperCamelCase							, expected_max_diff=0.01     )






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									_lowercase									=				torch_device == """cpu"""
									_lowercase									=				True
									_lowercase									=				[
									    """prior_num_inference_steps""",
									    """decoder_num_inference_steps""",
									    """super_res_num_inference_steps""",
									]

									self._test_inference_batch_single_identical(
									    test_max_difference=__UpperCamelCase							, relax_max_difference=__UpperCamelCase							, additional_params_copy_to_batched_inputs=__UpperCamelCase							, )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				[
									    """prior_num_inference_steps""",
									    """decoder_num_inference_steps""",
									    """super_res_num_inference_steps""",
									]

									if torch_device == "mps":
															# TODO: MPS errors with larger batch sizes
															_lowercase									=				[2, 3]
															self._test_inference_batch_consistent(
															    batch_sizes=__UpperCamelCase							, additional_params_copy_to_batched_inputs=__UpperCamelCase							, )
									else:
															self._test_inference_batch_consistent(
															    additional_params_copy_to_batched_inputs=__UpperCamelCase     )






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									return super().test_save_load_local()






			@skip_mps
			def       UpperCamelCase_							(			self     ):
									return super().test_save_load_optional_components()







@nightly
class       a_   ( unittest.TestCase						):






			def       UpperCamelCase_							(			self     ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/unclip/karlo_v1_alpha_horse_cpu.npy"""     )

									_lowercase									=				UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""     )
									pipeline.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				torch.manual_seed(0     )
									_lowercase									=				pipeline(
									    """horse"""							, num_images_per_prompt=1							, generator=__UpperCamelCase							, output_type="""np"""							, )

									_lowercase									=				output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image     ).max() < 1E-1







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






			def       UpperCamelCase_							(			self     ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/unclip/karlo_v1_alpha_horse_fp16.npy"""     )

									_lowercase									=				UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""							, torch_dtype=torch.floataa     )
									_lowercase									=				pipeline.to(__UpperCamelCase     )
									pipeline.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				torch.Generator(device="""cpu"""     ).manual_seed(0     )
									_lowercase									=				pipeline(
									    """horse"""							, generator=__UpperCamelCase							, output_type="""np"""							, )

									_lowercase									=				output.images[0]

									assert image.shape == (256, 256, 3)

									assert_mean_pixel_difference(__UpperCamelCase							, __UpperCamelCase     )






			def       UpperCamelCase_							(			self     ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_lowercase									=				UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""							, torch_dtype=torch.floataa     )
									_lowercase									=				pipe.to(__UpperCamelCase     )
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									_lowercase									=				pipe(
									    """horse"""							, num_images_per_prompt=1							, prior_num_inference_steps=2							, decoder_num_inference_steps=2							, super_res_num_inference_steps=2							, output_type="""np"""							, )

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