






import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			FlaxStableDiffusionPipeline.from_pretrained(
								    '''stabilityai/stable-diffusion-2''',  revision='''bf16''',  dtype=jnp.bfloataa,  )

								lowercase__    =			'''A painting of a squirrel eating a burger'''
								lowercase__    =			jax.device_count()
								lowercase__    =			num_samples * [prompt]
								lowercase__    =			sd_pipe.prepare_inputs(lowerCamelCase  )

								lowercase__    =			replicate(lowerCamelCase  )
								lowercase__    =			shard(lowerCamelCase  )

								lowercase__    =			jax.random.PRNGKey(0  )
								lowercase__    =			jax.random.split(lowerCamelCase,  jax.device_count()  )

								lowercase__    =			sd_pipe(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  num_inference_steps=25,  jit=lowerCamelCase  )[0]
								assert images.shape == (jax.device_count(), 1, 768, 768, 3)

								lowercase__    =			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
								lowercase__    =			images[0, 253:256, 253:256, -1]

								lowercase__    =			jnp.asarray(jax.device_get(image_slice.flatten()  )  )
								lowercase__    =			jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]  )
								print(F"""output_slice: {output_slice}"""  )
								assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			'''stabilityai/stable-diffusion-2'''
								lowercase__ ,						lowercase__    =			FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCamelCase,  subfolder='''scheduler'''  )
								lowercase__ ,						lowercase__    =			FlaxStableDiffusionPipeline.from_pretrained(
								    lowerCamelCase,  scheduler=lowerCamelCase,  revision='''bf16''',  dtype=jnp.bfloataa,  )
								lowercase__    =			scheduler_params

								lowercase__    =			'''A painting of a squirrel eating a burger'''
								lowercase__    =			jax.device_count()
								lowercase__    =			num_samples * [prompt]
								lowercase__    =			sd_pipe.prepare_inputs(lowerCamelCase  )

								lowercase__    =			replicate(lowerCamelCase  )
								lowercase__    =			shard(lowerCamelCase  )

								lowercase__    =			jax.random.PRNGKey(0  )
								lowercase__    =			jax.random.split(lowerCamelCase,  jax.device_count()  )

								lowercase__    =			sd_pipe(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  num_inference_steps=25,  jit=lowerCamelCase  )[0]
								assert images.shape == (jax.device_count(), 1, 768, 768, 3)

								lowercase__    =			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
								lowercase__    =			images[0, 253:256, 253:256, -1]

								lowercase__    =			jnp.asarray(jax.device_get(image_slice.flatten()  )  )
								lowercase__    =			jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]  )
								print(F"""output_slice: {output_slice}"""  )
								assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2












import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)
class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Optional[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str=14,  lowerCamelCase						:    List[str]=7,  lowerCamelCase						:    Dict=True,  lowerCamelCase						:    List[str]=True,  lowerCamelCase						:    Optional[Any]=True,  lowerCamelCase						:    Optional[Any]=True,  lowerCamelCase						:    Any=True,  lowerCamelCase						:    List[str]=99,  lowerCamelCase						:    Optional[Any]=32,  lowerCamelCase						:    Optional[int]=5,  lowerCamelCase						:    List[Any]=4,  lowerCamelCase						:    List[Any]=37,  lowerCamelCase						:    List[str]="gelu",  lowerCamelCase						:    Any=0.1,  lowerCamelCase						:    Optional[Any]=0.1,  lowerCamelCase						:    List[Any]=512,  lowerCamelCase						:    List[str]=16,  lowerCamelCase						:    Dict=2,  lowerCamelCase						:    Union[str, Any]=0.02,  lowerCamelCase						:    Optional[int]=3,  lowerCamelCase						:    Dict=4,  lowerCamelCase						:    List[Any]=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			seq_length
								lowercase__    =			is_training
								lowercase__    =			use_token_type_ids
								lowercase__    =			use_input_mask
								lowercase__    =			use_labels
								lowercase__    =			use_mc_token_ids
								lowercase__    =			vocab_size
								lowercase__    =			hidden_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_labels
								lowercase__    =			num_choices
								lowercase__    =			scope
								lowercase__    =			self.vocab_size - 1




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

								lowercase__    =			None
								if self.use_input_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__    =			None
								if self.use_mc_token_ids:
												lowercase__    =			ids_tensor([self.batch_size, self.num_choices],  self.seq_length  )

								lowercase__    =			None
								lowercase__    =			None
								lowercase__    =			None
								if self.use_labels:
												lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
												lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.num_labels  )
												lowercase__    =			ids_tensor([self.batch_size],  self.num_choices  )

								lowercase__    =			self.get_config()

								lowercase__    =			ids_tensor([self.num_hidden_layers, self.num_attention_heads],  2  )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								return CTRLConfig(
								    vocab_size=self.vocab_size,  n_embd=self.hidden_size,  n_layer=self.num_hidden_layers,  n_head=self.num_attention_heads,  n_positions=self.max_position_embeddings,  pad_token_id=self.pad_token_id,  )




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    Union[str, Any],  *lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			CTRLModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								model(lowerCamelCase,  token_type_ids=lowerCamelCase,  head_mask=lowerCamelCase  )
								model(lowerCamelCase,  token_type_ids=lowerCamelCase  )
								lowercase__    =			model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )
								self.parent.assertEqual(len(result.past_key_values  ),  config.n_layer  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[int],  *lowerCamelCase						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.loss.shape,  ()  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()

								(
								    (
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						
								)    =			config_and_inputs

								lowercase__    =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

								return config, inputs_dict




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    int,  lowerCamelCase						:    Any,  *lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			CTRLForSequenceClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
				lowercase__											=       (CTRLLMHeadModel,) if is_torch_available() else ()
				lowercase__											=       (
				    {
				        """feature-extraction""": CTRLModel,
				        """text-classification""": CTRLForSequenceClassification,
				        """text-generation""": CTRLLMHeadModel,
				        """zero-shot""": CTRLForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__											=       True
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
												# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
												# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
												# config could not be created.
												return True

								return False




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=lowerCamelCase,  n_embd=37  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*lowerCamelCase  )




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								pass




				@slow
				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			CTRLModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )




				@unittest.skip('''The model doesn\'t support left padding'''  )  # and it's not used enough to be worth fixing :)
				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								pass



@require_torch
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()




				@slow
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel.from_pretrained('''ctrl'''  )
								model.to(lowerCamelCase  )
								lowercase__    =			torch.tensor(
								    [[11_859, 0, 1_611, 8]],  dtype=torch.long,  device=lowerCamelCase  )  # Legal the president is
								lowercase__    =			[
								    11_859,
								    0,
								    1_611,
								    8,
								    5,
								    150,
								    26_449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2_595,
								    48,
								    20_740,
								    246_533,
								    246_533,
								    19,
								    30,
								    5,
								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

								lowercase__    =			model.generate(lowerCamelCase,  do_sample=lowerCamelCase  )
								self.assertListEqual(output_ids[0].tolist(),  lowerCamelCase  )




