





'''simple docstring'''




import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
			import jax
			import jax.numpy as jnp






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





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Dict:
		return F'''gaussian_noise_s={seed}_shape={'_'.join([str(UpperCamelCase_		) for s in shape]		)}.npy'''





	def   _snake_case   (		self		)      ->		str:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()





	def   _snake_case   (		self      ,       UpperCamelCase_=0      ,       UpperCamelCase_=(4, 4, 64, 64)      ,       UpperCamelCase_=False		)      ->		Dict:
		snake_case__	       =   jnp.bfloataa if fpaa else jnp.floataa
		snake_case__	       =   jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase_      ,       UpperCamelCase_		)		)      ,       dtype=UpperCamelCase_		)
		return image





	def   _snake_case   (		self      ,       UpperCamelCase_=False      ,       UpperCamelCase_="CompVis/stable-diffusion-v1-4"		)      ->		Dict:
		snake_case__	       =   jnp.bfloataa if fpaa else jnp.floataa
		snake_case__	       =   'bf16' if fpaa else None

		snake_case__       ,  snake_case__	       =   FlaxUNetaDConditionModel.from_pretrained(
		    UpperCamelCase_      ,       subfolder='unet'      ,       dtype=UpperCamelCase_      ,       revision=UpperCamelCase_		)
		return model, params





	def   _snake_case   (		self      ,       UpperCamelCase_=0      ,       UpperCamelCase_=(4, 77, 768)      ,       UpperCamelCase_=False		)      ->		Any:
		snake_case__	       =   jnp.bfloataa if fpaa else jnp.floataa
		snake_case__	       =   jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase_      ,       UpperCamelCase_		)		)      ,       dtype=UpperCamelCase_		)
		return hidden_states





	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
	        [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
	        [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
	        [3, 1000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
	        # fmt: on
	    ]		)
	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		List[str]:
		snake_case__       ,  snake_case__	       =   self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'      ,       fpaa=UpperCamelCase_		)
		snake_case__	       =   self.get_latents(UpperCamelCase_      ,       fpaa=UpperCamelCase_		)
		snake_case__	       =   self.get_encoder_hidden_states(UpperCamelCase_      ,       fpaa=UpperCamelCase_		)

		snake_case__	       =   model.apply(
		    {'params': params}      ,       UpperCamelCase_      ,       jnp.array(UpperCamelCase_      ,       dtype=jnp.intaa		)      ,       encoder_hidden_states=UpperCamelCase_      ,       ).sample

		assert sample.shape == latents.shape

		snake_case__	       =   jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)      ,       dtype=jnp.floataa		)
		snake_case__	       =   jnp.array(UpperCamelCase_      ,       dtype=jnp.floataa		)

		# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
		assert jnp.allclose(UpperCamelCase_      ,       UpperCamelCase_      ,       atol=1E-2		)





	@parameterized.expand(
	    [
	        # fmt: off
	        [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
	        [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
	        [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
	        [3, 1000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
	        # fmt: on
	    ]		)
	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[int]:
		snake_case__       ,  snake_case__	       =   self.get_unet_model(model_id='stabilityai/stable-diffusion-2'      ,       fpaa=UpperCamelCase_		)
		snake_case__	       =   self.get_latents(UpperCamelCase_      ,       shape=(4, 4, 96, 96)      ,       fpaa=UpperCamelCase_		)
		snake_case__	       =   self.get_encoder_hidden_states(UpperCamelCase_      ,       shape=(4, 77, 1024)      ,       fpaa=UpperCamelCase_		)

		snake_case__	       =   model.apply(
		    {'params': params}      ,       UpperCamelCase_      ,       jnp.array(UpperCamelCase_      ,       dtype=jnp.intaa		)      ,       encoder_hidden_states=UpperCamelCase_      ,       ).sample

		assert sample.shape == latents.shape

		snake_case__	       =   jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)      ,       dtype=jnp.floataa		)
		snake_case__	       =   jnp.array(UpperCamelCase_      ,       dtype=jnp.floataa		)

		# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
		assert jnp.allclose(UpperCamelCase_      ,       UpperCamelCase_      ,       atol=1E-2		)










'''simple docstring'''




import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)






class 				__snake_case     (					unittest.TestCase							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=7      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=99      ,       UpperCamelCase_=32      ,       UpperCamelCase_=5      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=512      ,       UpperCamelCase_=16      ,       UpperCamelCase_=2      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=4      ,       )      ->		List[Any]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   seq_length
		snake_case__	       =   is_training
		snake_case__	       =   use_attention_mask
		snake_case__	       =   use_token_type_ids
		snake_case__	       =   use_labels
		snake_case__	       =   vocab_size
		snake_case__	       =   hidden_size
		snake_case__	       =   num_hidden_layers
		snake_case__	       =   num_attention_heads
		snake_case__	       =   intermediate_size
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   type_vocab_size
		snake_case__	       =   type_sequence_label_size
		snake_case__	       =   initializer_range
		snake_case__	       =   num_choices





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size		)

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

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

		snake_case__	       =   RobertaConfig(
		    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=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       )

		return config, input_ids, token_type_ids, attention_mask





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs
		snake_case__	       =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
		return config, inputs_dict





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs

		snake_case__	       =   True
		snake_case__	       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       vocab_size=2		)

		return (
		    config,
		    input_ids,
		    token_type_ids,
		    encoder_hidden_states,
		    encoder_attention_mask,
		)






@require_flax
class 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  True

	__lowerCAmelCase       						=  (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   FlaxRobertaModelTester(self		)





	@slow
	def   _snake_case   (		self		)      ->		Tuple:
		for model_class_name in self.all_model_classes:
			snake_case__	       =   model_class_name.from_pretrained('roberta-base'      ,       from_pt=UpperCamelCase_		)
			snake_case__	       =   model(np.ones((1, 1)		)		)
			self.assertIsNotNone(UpperCamelCase_		)



