





import unittest

import numpy as np

from transformers import RoFormerConfig, 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():
				import jax.numpy as jnp

				from transformers.models.roformer.modeling_flax_roformer import (
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				)



class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def __init__(      self				:     str							,	a				:     Union[str, Any]							,	a				:     str=13							,	a				:     Union[str, Any]=7							,	a				:     Optional[int]=True							,	a				:     Dict=True							,	a				:     int=True							,	a				:     List[str]=True							,	a				:     Dict=99							,	a				:     Any=32							,	a				:     List[Any]=5							,	a				:     Any=4							,	a				:     Optional[Any]=37							,	a				:     Optional[Any]="gelu"							,	a				:     int=0.1							,	a				:     int=0.1							,	a				:     List[Any]=5_12							,	a				:     List[str]=16							,	a				:     Dict=2							,	a				:     List[str]=0.02							,	a				:     List[Any]=4							,	)	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					parent
													SCREAMING_SNAKE_CASE__     :				int   =					batch_size
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					seq_length
													SCREAMING_SNAKE_CASE__     :				List[str]   =					is_training
													SCREAMING_SNAKE_CASE__     :				List[str]   =					use_attention_mask
													SCREAMING_SNAKE_CASE__     :				List[str]   =					use_token_type_ids
													SCREAMING_SNAKE_CASE__     :				List[str]   =					use_labels
													SCREAMING_SNAKE_CASE__     :				Any   =					vocab_size
													SCREAMING_SNAKE_CASE__     :				int   =					hidden_size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					num_hidden_layers
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					num_attention_heads
													SCREAMING_SNAKE_CASE__     :				Dict   =					intermediate_size
													SCREAMING_SNAKE_CASE__     :				Dict   =					hidden_act
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					hidden_dropout_prob
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__     :				Any   =					max_position_embeddings
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					type_vocab_size
													SCREAMING_SNAKE_CASE__     :				int   =					type_sequence_label_size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					initializer_range
													SCREAMING_SNAKE_CASE__     :				str   =					num_choices

							def      A_  (      self				:     Union[str, Any]    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size    )

													SCREAMING_SNAKE_CASE__     :				int   =					None
													if self.use_attention_mask:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					random_attention_mask([self.batch_size, self.seq_length]    )

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

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					RoFormerConfig(
													    vocab_size=self.vocab_size							,	hidden_size=self.hidden_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							,	is_decoder=a							,	initializer_range=self.initializer_range							,	)

													return config, input_ids, token_type_ids, attention_mask






							def      A_  (      self				:     Union[str, Any]    )	->str:
													SCREAMING_SNAKE_CASE__     :				Any   =					self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					config_and_inputs
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
													return config, inputs_dict







@require_flax
class    _a     (				lowercase__					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						True

							snake_case_							 =						(
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)

							def      A_  (      self				:     Optional[Any]    )	->str:
													SCREAMING_SNAKE_CASE__     :				int   =					FlaxRoFormerModelTester(self    )






							@slow
							def      A_  (      self				:     Optional[int]    )	->Optional[Any]:
													for model_class_name in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__     :				str   =					model_class_name.from_pretrained("junnyu/roformer_chinese_small"							,	from_pt=a    )
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					model(np.ones((1, 1)    )    )
																			self.assertIsNotNone(a    )







@require_flax
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							@slow
							def      A_  (      self				:     Any    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					jnp.array([[0, 1, 2, 3, 4, 5]]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					model(a    )[0]

													SCREAMING_SNAKE_CASE__     :				Dict   =					5_00_00

													SCREAMING_SNAKE_CASE__     :				Any   =					(1, 6, vocab_size)
													self.assertEqual(output.shape							,	a    )

													SCREAMING_SNAKE_CASE__     :				str   =					jnp.array(
													    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]    )

													self.assertTrue(jnp.allclose(output[:, :3, :3]							,	a							,	atol=1E-4    )    )






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax
				import jax.numpy as jnp
				from flax.jax_utils import replicate
				from flax.training.common_utils import shard



@slow
@require_flax
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def      A_  (      self				:     Dict    )	->List[Any]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()

							def      A_  (      self				:     Dict    )	->Tuple:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-canny"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Dict   =					"bird"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe.prepare_image_inputs([canny_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				int   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Tuple   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					jnp.array(
													    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






							def      A_  (      self				:     List[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-openpose"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Any   =					"Chef in the kitchen"
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"    )
													SCREAMING_SNAKE_CASE__     :				str   =					pipe.prepare_image_inputs([pose_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Any   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				str   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.array(
													    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2