
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       =(CMStochasticIterativeScheduler,)
							lowercase_ : str       =10




							def 				A__   (       self     ,**A__):
												lowercase					=		{
												    '''num_train_timesteps''': 2_0_1,
												    '''sigma_min''': 0.002,
												    '''sigma_max''': 80.0,
												}

												config.update(**A__)
												return config




							def 				A__   (       self):
												lowercase					=		1_0

												lowercase					=		self.get_scheduler_config()
												lowercase					=		self.scheduler_classes[0](**A__)

												scheduler.set_timesteps(A__)

												lowercase					=		scheduler.timesteps[0]
												lowercase					=		scheduler.timesteps[1]

												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample

												lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample
												lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

												self.assertEqual(output_a.shape     ,sample.shape)
												self.assertEqual(output_a.shape     ,output_a.shape)




							def 				A__   (       self):
												for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=A__)




							def 				A__   (       self):
												for clip_denoised in [True, False]:
																	self.check_over_configs(clip_denoised=A__)




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config()
												lowercase					=		scheduler_class(**A__)

												lowercase					=		1
												scheduler.set_timesteps(A__)
												lowercase					=		scheduler.timesteps

												lowercase					=		torch.manual_seed(0)

												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(A__):
																	# 1. scale model input
																	lowercase					=		scheduler.scale_model_input(A__     ,A__)

																	# 2. predict noise residual
																	lowercase					=		model(A__     ,A__)

																	# 3. predict previous sample x_t-1
																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,generator=A__).prev_sample

																	lowercase					=		pred_prev_sample

												lowercase					=		torch.sum(torch.abs(A__))
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_sum.item() - 192.7614) < 1E-2
												assert abs(result_mean.item() - 0.2510) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config()
												lowercase					=		scheduler_class(**A__)

												lowercase					=		[1_0_6, 0]
												scheduler.set_timesteps(timesteps=A__)
												lowercase					=		scheduler.timesteps

												lowercase					=		torch.manual_seed(0)

												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter * scheduler.init_noise_sigma

												for t in timesteps:
																	# 1. scale model input
																	lowercase					=		scheduler.scale_model_input(A__     ,A__)

																	# 2. predict noise residual
																	lowercase					=		model(A__     ,A__)

																	# 3. predict previous sample x_t-1
																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,generator=A__).prev_sample

																	lowercase					=		pred_prev_sample

												lowercase					=		torch.sum(torch.abs(A__))
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_sum.item() - 347.6357) < 1E-2
												assert abs(result_mean.item() - 0.4527) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config()
												lowercase					=		scheduler_class(**A__)

												lowercase					=		[3_9, 3_0, 1_2, 1_5, 0]

												with self.assertRaises(A__     ,msg='''`timesteps` must be in descending order.'''):
																	scheduler.set_timesteps(timesteps=A__)




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config()
												lowercase					=		scheduler_class(**A__)

												lowercase					=		[3_9, 3_0, 1_2, 1, 0]
												lowercase					=		len(A__)

												with self.assertRaises(A__     ,msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''):
																	scheduler.set_timesteps(num_inference_steps=A__     ,timesteps=A__)





							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config()
												lowercase					=		scheduler_class(**A__)

												lowercase					=		[scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    A__     ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,):
																	scheduler.set_timesteps(timesteps=A__)






import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
							import os

							import jax.numpy as jnp
							from jax import jit

							from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
							from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

							lowercase__	:Optional[Any]										=					"0.12"  # assumed parallelism: 8


if is_torch_available():
							import torch



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					if rng is None:
										lowercase					=		random.Random()

					lowercase					=		1
					for dim in shape:
										total_dims *= dim

					lowercase					=		[]
					for _ in range(lowerCAmelCase__	):
										values.append(rng.randint(0				,    vocab_size - 1	)	)

					lowercase					=		np.array(lowerCAmelCase__				,    dtype=jnp.intaa	).reshape(lowerCAmelCase__	)

					return output



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					lowercase					=		ids_tensor(lowerCAmelCase__				,    vocab_size=2				,    rng=lowerCAmelCase__	)
					# make sure that at least one token is attended to for each batch
					lowercase					=		1
					return attn_mask







@require_flax
class        lowercase	:
							lowercase_ : Any       =None
							lowercase_ : List[str]       =()




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

												# cut to half length & take max batch_size 3
												lowercase					=		2
												lowercase					=		inputs['''input_ids'''].shape[-1] // 2
												lowercase					=		inputs['''input_ids'''][:max_batch_size, :sequence_length]

												lowercase					=		jnp.ones_like(A__)
												lowercase					=		attention_mask[:max_batch_size, :sequence_length]

												# generate max 5 tokens
												lowercase					=		input_ids.shape[-1] + 5
												if config.eos_token_id is not None and config.pad_token_id is None:
																	# hack to allow generate for models such as GPT2 as is done in `generate()`
																	lowercase					=		config.eos_token_id
												return config, input_ids, attention_mask, max_length




							@is_pt_flax_cross_test
							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		0

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	lowercase					=		getattr(A__     ,A__)
																	lowercase					=		pt_model_class(A__).eval()
																	lowercase					=		load_flax_weights_in_pytorch_model(A__     ,flax_model.params)

																	lowercase					=		flax_model.generate(A__).sequences
																	lowercase					=		pt_model.generate(torch.tensor(A__     ,dtype=torch.long))

																	if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																						lowercase					=		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																	self.assertListEqual(pt_generation_outputs.numpy().tolist()     ,flax_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		True
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		2

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		2
												lowercase					=		2

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[0]     ,input_ids.shape[0] * config.num_return_sequences)




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		True
												lowercase					=		max_length
												lowercase					=		0.8
												lowercase					=		1_0
												lowercase					=		0.3
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		max_length
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		max_length
												lowercase					=		2
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		False
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		True
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())





							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		2
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())







@require_flax
class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												lowercase					=		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''')
												lowercase					=		FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')

												lowercase					=		'''Hello world'''
												lowercase					=		tokenizer(A__     ,return_tensors='''np''').input_ids

												# typos are quickly detected (the correct argument is `do_sample`)
												with self.assertRaisesRegex(A__     ,'''do_samples'''):
																	model.generate(A__     ,do_samples=A__)

												# arbitrary arguments that will not be used anywhere are also not accepted
												with self.assertRaisesRegex(A__     ,'''foo'''):
																	lowercase					=		{'''foo''': '''bar'''}
																	model.generate(A__     ,**A__)




