

import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Union[str, Any]      							=					DiTPipeline
			_UpperCAmelCase       :							Union[str, Any]      							=					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
			_UpperCAmelCase       :							int      							=					PipelineTesterMixin.required_optional_params - {
			    '''latents''',
			    '''num_images_per_prompt''',
			    '''callback''',
			    '''callback_steps''',
			}
			_UpperCAmelCase       :							Dict      							=					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
			_UpperCAmelCase       :							List[Any]      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			str  		=	TransformeraDModel(
									    sample_size=16       ,						num_layers=2       ,						patch_size=4       ,						attention_head_dim=8       ,						num_attention_heads=2       ,						in_channels=4       ,						out_channels=8       ,						attention_bias=lowerCAmelCase__       ,						activation_fn="gelu-approximate"       ,						num_embeds_ada_norm=1000       ,						norm_type="ada_norm_zero"       ,						norm_elementwise_affine=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoencoderKL()
									SCREAMING_SNAKE_CASE_:			int  		=	DDIMScheduler()
									SCREAMING_SNAKE_CASE_:			Dict  		=	{"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
									return components



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[str]=0):
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_:			Any  		=	torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	{
									    "class_labels": [1],
									    "generator": generator,
									    "num_inference_steps": 2,
									    "output_type": "numpy",
									}
									return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			Any  		=	"cpu"

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.get_dummy_components()
									SCREAMING_SNAKE_CASE_:			str  		=	self.pipeline_class(**lowerCAmelCase__)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Any  		=	image[0, -3:, -3:, -1]

									self.assertEqual(image.shape       ,						(1, 16, 16, 3))
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457])
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	np.abs(image_slice.flatten() - expected_slice).max()
									self.assertLessEqual(lowerCAmelCase__       ,						1E-3)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									self._test_inference_batch_single_identical(relax_max_difference=lowerCAmelCase__       ,						expected_max_diff=1E-3)




			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()       ,						reason="XFormers attention is only available with CUDA and `xformers` installed"       ,						)
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)







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




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.manual_seed(0)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	DiTPipeline.from_pretrained("facebook/DiT-XL-2-256")
									pipe.to("cuda")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	["vase", "umbrella", "white shark", "white wolf"]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pipe.get_label_ids(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pipe(lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						num_inference_steps=40       ,						output_type="np").images

									for word, image in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			str  		=	load_numpy(
															    F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy")
															assert np.abs((expected_image - image).max()) < 1E-2




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Any  		=	DiTPipeline.from_pretrained("facebook/DiT-XL-2-512")
									SCREAMING_SNAKE_CASE_:			str  		=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
									pipe.to("cuda")

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	["vase", "umbrella"]
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	pipe.get_label_ids(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	pipe(lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						num_inference_steps=25       ,						output_type="np").images

									for word, image in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_numpy(
															    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
															    F"/dit/{word}_512.npy")

															assert np.abs((expected_image - image).max()) < 1E-1





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


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

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
						import torch



class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					str       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Dict=14       ,						lowerCAmelCase__  :					Optional[Any]=7       ,						lowerCAmelCase__  :					Optional[int]=True       ,						lowerCAmelCase__  :					Optional[int]=True       ,						lowerCAmelCase__  :					Union[str, Any]=False       ,						lowerCAmelCase__  :					Tuple=True       ,						lowerCAmelCase__  :					Optional[int]=99       ,						lowerCAmelCase__  :					str=32       ,						lowerCAmelCase__  :					Any=4       ,						lowerCAmelCase__  :					Any=4       ,						lowerCAmelCase__  :					Any=4       ,						lowerCAmelCase__  :					Dict=37       ,						lowerCAmelCase__  :					Tuple="gelu"       ,						lowerCAmelCase__  :					str=0.1       ,						lowerCAmelCase__  :					Optional[Any]=0.1       ,						lowerCAmelCase__  :					Dict=512       ,						lowerCAmelCase__  :					Dict=0.02       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	parent
									SCREAMING_SNAKE_CASE_:			Any  		=	batch_size
									SCREAMING_SNAKE_CASE_:			str  		=	seq_length
									SCREAMING_SNAKE_CASE_:			Dict  		=	is_training
									SCREAMING_SNAKE_CASE_:			str  		=	use_input_mask
									SCREAMING_SNAKE_CASE_:			int  		=	use_token_type_ids
									SCREAMING_SNAKE_CASE_:			Tuple  		=	use_labels
									SCREAMING_SNAKE_CASE_:			int  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	rotary_dim
									SCREAMING_SNAKE_CASE_:			Dict  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	num_attention_heads
									SCREAMING_SNAKE_CASE_:			Dict  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			List[str]  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			List[str]  		=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:			int  		=	attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			int  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	None
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	vocab_size - 1
									SCREAMING_SNAKE_CASE_:			Tuple  		=	vocab_size - 1
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	vocab_size - 1



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	ids_tensor([self.batch_size, self.seq_length]       ,						self.vocab_size)

									SCREAMING_SNAKE_CASE_:			Any  		=	None
									if self.use_input_mask:
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	random_attention_mask([self.batch_size, self.seq_length])

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	GPTJConfig(
									    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       ,						use_cache=lowerCAmelCase__       ,						bos_token_id=self.bos_token_id       ,						eos_token_id=self.eos_token_id       ,						pad_token_id=self.pad_token_id       ,						rotary_dim=self.rotary_dim       ,						)

									return (config, input_ids, input_mask)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	config_and_inputs
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{"input_ids": input_ids, "attention_mask": attention_mask}
									return config, inputs_dict



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	20
									SCREAMING_SNAKE_CASE_:			Any  		=	model_class_name(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	model.init_cache(input_ids.shape[0]       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	jnp.ones((input_ids.shape[0], max_decoder_length)       ,						dtype="i4")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,						(input_ids.shape[0], input_ids.shape[-1] - 1))
									SCREAMING_SNAKE_CASE_:			int  		=	model(
									    input_ids[:, :-1]       ,						attention_mask=lowerCAmelCase__       ,						past_key_values=lowerCAmelCase__       ,						position_ids=lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,						dtype="i4")
									SCREAMING_SNAKE_CASE_:			Dict  		=	model(
									    input_ids[:, -1:]       ,						attention_mask=lowerCAmelCase__       ,						past_key_values=outputs_cache.past_key_values       ,						position_ids=lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	model(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
									self.parent.assertTrue(diff < 1E-3       ,						msg=F"Max diff is {diff}")




			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Tuple):
									SCREAMING_SNAKE_CASE_:			int  		=	20
									SCREAMING_SNAKE_CASE_:			Tuple  		=	model_class_name(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]       ,						axis=-1       ,						)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	model.init_cache(input_ids.shape[0]       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,						(input_ids.shape[0], input_ids.shape[-1] - 1))

									SCREAMING_SNAKE_CASE_:			Any  		=	model(
									    input_ids[:, :-1]       ,						attention_mask=lowerCAmelCase__       ,						past_key_values=lowerCAmelCase__       ,						position_ids=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			int  		=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,						dtype="i4")
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model(
									    input_ids[:, -1:]       ,						past_key_values=outputs_cache.past_key_values       ,						attention_mask=lowerCAmelCase__       ,						position_ids=lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model(lowerCAmelCase__       ,						attention_mask=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
									self.parent.assertTrue(diff < 1E-3       ,						msg=F"Max diff is {diff}")







@require_flax
class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Dict      							=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
			_UpperCAmelCase       :							Tuple      							=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	FlaxGPTJModelTester(self)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			str  		=	self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)



			@tooslow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	GPTaTokenizer.from_pretrained("gpt2"       ,						pad_token="<|endoftext|>"       ,						padding_side="left")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer(["Hello this is a long string", "Hey"]       ,						return_tensors="np"       ,						padding=lowerCAmelCase__       ,						truncation=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
									SCREAMING_SNAKE_CASE_:			int  		=	False
									SCREAMING_SNAKE_CASE_:			str  		=	model.config.eos_token_id

									SCREAMING_SNAKE_CASE_:			int  		=	jax.jit(model.generate)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	jit_generate(
									    inputs["input_ids"]       ,						attention_mask=inputs["attention_mask"]       ,						pad_token_id=tokenizer.pad_token_id).sequences

									SCREAMING_SNAKE_CASE_:			int  		=	tokenizer.batch_decode(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	[
									    "Hello this is a long string of text.\n\nI'm trying to get the text of the",
									    "Hey, I'm a little late to the party. I'm going to",
									]

									self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			@is_pt_flax_cross_test
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__):
																					# prepare inputs
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	self._prepare_for_class(lowerCAmelCase__       ,						lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					SCREAMING_SNAKE_CASE_:			int  		=	getattr(lowerCAmelCase__       ,						lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	pt_inputs["input_ids"].shape
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	np.random.randint(0       ,						seq_length - 1       ,						size=(batch_size,))
																					for batch_idx, start_index in enumerate(lowerCAmelCase__):
																											SCREAMING_SNAKE_CASE_:			Dict  		=	0
																											SCREAMING_SNAKE_CASE_:			str  		=	1
																											SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	0
																											SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	1
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pt_model_class(lowerCAmelCase__).eval()
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	model_class(lowerCAmelCase__       ,						dtype=jnp.floataa)

																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,						lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			List[str]  		=	fx_state

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pt_model(**lowerCAmelCase__).to_tuple()

																					SCREAMING_SNAKE_CASE_:			List[str]  		=	fx_model(**lowerCAmelCase__).to_tuple()
																					self.assertEqual(len(lowerCAmelCase__)       ,						len(lowerCAmelCase__)       ,						"Output lengths differ between Flax and PyTorch")
																					for fx_output, pt_output in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
																											self.assert_almost_equals(fx_output[:, -1]       ,						pt_output[:, -1].numpy()       ,						4E-2)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											pt_model.save_pretrained(lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_:			int  		=	model_class.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:			List[str]  		=	fx_model_loaded(**lowerCAmelCase__).to_tuple()
																					self.assertEqual(
																					    len(lowerCAmelCase__)       ,						len(lowerCAmelCase__)       ,						"Output lengths differ between Flax and PyTorch")
																					for fx_output_loaded, pt_output in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
																											self.assert_almost_equals(fx_output_loaded[:, -1]       ,						pt_output[:, -1].numpy()       ,						4E-2)



			@is_pt_flax_cross_test
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__):
																					# prepare inputs
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self._prepare_for_class(lowerCAmelCase__       ,						lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					SCREAMING_SNAKE_CASE_:			Any  		=	getattr(lowerCAmelCase__       ,						lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pt_model_class(lowerCAmelCase__).eval()
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	model_class(lowerCAmelCase__       ,						dtype=jnp.floataa)

																					SCREAMING_SNAKE_CASE_:			int  		=	load_flax_weights_in_pytorch_model(lowerCAmelCase__       ,						fx_model.params)
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	pt_inputs["input_ids"].shape
																					SCREAMING_SNAKE_CASE_:			Any  		=	np.random.randint(0       ,						seq_length - 1       ,						size=(batch_size,))
																					for batch_idx, start_index in enumerate(lowerCAmelCase__):
																											SCREAMING_SNAKE_CASE_:			str  		=	0
																											SCREAMING_SNAKE_CASE_:			Tuple  		=	1
																											SCREAMING_SNAKE_CASE_:			Dict  		=	0
																											SCREAMING_SNAKE_CASE_:			str  		=	1

																					# make sure weights are tied in PyTorch
																					pt_model.tie_weights()

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pt_model(**lowerCAmelCase__).to_tuple()

																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	fx_model(**lowerCAmelCase__).to_tuple()
																					self.assertEqual(len(lowerCAmelCase__)       ,						len(lowerCAmelCase__)       ,						"Output lengths differ between Flax and PyTorch")
																					for fx_output, pt_output in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
																											self.assert_almost_equals(fx_output[:, -1]       ,						pt_output[:, -1].numpy()       ,						4E-2)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_:			Any  		=	pt_model_class.from_pretrained(lowerCAmelCase__       ,						from_flax=lowerCAmelCase__)

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:			Any  		=	pt_model_loaded(**lowerCAmelCase__).to_tuple()

																					self.assertEqual(
																					    len(lowerCAmelCase__)       ,						len(lowerCAmelCase__)       ,						"Output lengths differ between Flax and PyTorch")
																					for fx_output, pt_output in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
																											self.assert_almost_equals(fx_output[:, -1]       ,						pt_output[:, -1].numpy()       ,						4E-2)




			@tooslow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	model_class_name.from_pretrained("EleutherAI/gpt-j-6B")
															SCREAMING_SNAKE_CASE_:			str  		=	model(np.ones((1, 1)))
															self.assertIsNotNone(lowerCAmelCase__)






