






"""simple docstring"""






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						(DDPMParallelScheduler,)
      def _SCREAMING_SNAKE_CASE				(		self  :						str		,			**lowercase_  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''variance_type''': '''fixed_small''',
              '''clip_sample''': True,
          }

          config.update(**lowercase_)
          return config
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]		,			[0.0_02, 0.02, 0.2, 2]):
              self.check_over_configs(beta_start=lowercase_		,			beta_end=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          self.check_over_configs(thresholding=lowercase_)
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=lowercase_		,			prediction_type=lowercase_		,			sample_max_value=lowercase_		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          for t in [0, 500, 999]:
              self.check_over_forward(time_step=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_   :						int							=	self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_   :						Tuple							=	scheduler_class(**lowercase_)

          assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_09_79)) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_   :						Any							=	self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_   :						str							=	scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_   :						str							=	len(lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	self.dummy_model()
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.dummy_sample_deter
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.dummy_sample_deter + 0.1
          SCREAMING_SNAKE_CASE_   :						int							=	self.dummy_sample_deter - 0.1

          SCREAMING_SNAKE_CASE_   :						Tuple							=	samplea.shape[0]
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.stack([samplea, samplea, samplea]		,			dim=0)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.arange(lowercase_)[0:3, None].repeat(1		,			lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(samples.flatten(0		,			1)		,			timesteps.flatten(0		,			1))
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	scheduler.batch_step_no_noise(lowercase_		,			timesteps.flatten(0		,			1)		,			samples.flatten(0		,			1))

          SCREAMING_SNAKE_CASE_   :						int							=	torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 11_53.18_33) < 1e-2
          assert abs(result_mean.item() - 0.50_05) < 1e-3
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_   :						int							=	self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	len(lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.dummy_model()
          SCREAMING_SNAKE_CASE_   :						Tuple							=	self.dummy_sample_deter
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.manual_seed(0)

          for t in reversed(range(lowercase_)):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE_   :						Any							=	model(lowercase_		,			lowercase_)

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE_   :						Tuple							=	scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			generator=lowercase_).prev_sample

              SCREAMING_SNAKE_CASE_   :						int							=	pred_prev_sample

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 2_58.96_06) < 1e-2
          assert abs(result_mean.item() - 0.33_72) < 1e-3
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.get_scheduler_config(prediction_type='''v_prediction''')
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	len(lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.dummy_model()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.dummy_sample_deter
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.manual_seed(0)

          for t in reversed(range(lowercase_)):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	model(lowercase_		,			lowercase_)

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			generator=lowercase_).prev_sample

              SCREAMING_SNAKE_CASE_   :						Dict							=	pred_prev_sample

          SCREAMING_SNAKE_CASE_   :						int							=	torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 2_02.02_96) < 1e-2
          assert abs(result_mean.item() - 0.26_31) < 1e-3
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_   :						str							=	self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_   :						Tuple							=	scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	scheduler.timesteps

          for i, timestep in enumerate(lowercase_):
              if i == len(lowercase_) - 1:
                  SCREAMING_SNAKE_CASE_   :						Any							=	-1
              else:
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	timesteps[i + 1]

              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	scheduler.previous_timestep(lowercase_)
              SCREAMING_SNAKE_CASE_   :						int							=	prev_t.item()

              self.assertEqual(lowercase_		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_   :						Any							=	self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_   :						Any							=	scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	[100, 87, 50, 51, 0]

          with self.assertRaises(lowercase_		,			msg='''`custom_timesteps` must be in descending order.'''):
              scheduler.set_timesteps(timesteps=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[100, 87, 50, 1, 0]
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	len(lowercase_)

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



      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_   :						Dict							=	scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	[scheduler.config.num_train_timesteps]

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










"""simple docstring"""






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 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						int		,			lowercase_  :						List[Any]		,			lowercase_  :						int=14		,			lowercase_  :						Tuple=7		,			lowercase_  :						int=True		,			lowercase_  :						int=True		,			lowercase_  :						int=False		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						Union[str, Any]=99		,			lowercase_  :						Optional[int]=32		,			lowercase_  :						List[Any]=4		,			lowercase_  :						Optional[int]=4		,			lowercase_  :						List[str]=4		,			lowercase_  :						Tuple=37		,			lowercase_  :						Any="gelu"		,			lowercase_  :						Dict=0.1		,			lowercase_  :						str=0.1		,			lowercase_  :						Optional[int]=512		,			lowercase_  :						Dict=0.02		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	parent
          SCREAMING_SNAKE_CASE_   :						Any							=	batch_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	seq_length
          SCREAMING_SNAKE_CASE_   :						Any							=	is_training
          SCREAMING_SNAKE_CASE_   :						int							=	use_input_mask
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	use_token_type_ids
          SCREAMING_SNAKE_CASE_   :						Tuple							=	use_labels
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						str							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	rotary_dim
          SCREAMING_SNAKE_CASE_   :						str							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						int							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						str							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						Dict							=	None
          SCREAMING_SNAKE_CASE_   :						str							=	vocab_size - 1
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	vocab_size - 1
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	vocab_size - 1
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size)

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

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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=lowercase_		,			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  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	config_and_inputs
          SCREAMING_SNAKE_CASE_   :						int							=	{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Any		,			lowercase_  :						Any		,			lowercase_  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	20
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_class_name(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model.init_cache(input_ids.shape[0]		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.ones((input_ids.shape[0], max_decoder_length)		,			dtype='''i4''')

          SCREAMING_SNAKE_CASE_   :						Any							=	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=lowercase_		,			past_key_values=lowercase_		,			position_ids=lowercase_		,			)

          SCREAMING_SNAKE_CASE_   :						str							=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,			dtype='''i4''')
          SCREAMING_SNAKE_CASE_   :						int							=	model(
              input_ids[:, -1:]		,			attention_mask=lowercase_		,			past_key_values=outputs_cache.past_key_values		,			position_ids=lowercase_		,			)

          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	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  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Optional[int]		,			lowercase_  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	20
          SCREAMING_SNAKE_CASE_   :						List[str]							=	model_class_name(lowercase_)

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

          SCREAMING_SNAKE_CASE_   :						Tuple							=	model.init_cache(input_ids.shape[0]		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1)[None, :]		,			(input_ids.shape[0], input_ids.shape[-1] - 1))

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

          SCREAMING_SNAKE_CASE_   :						Any							=	model(lowercase_		,			attention_mask=lowercase_)

          SCREAMING_SNAKE_CASE_   :						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}')




@require_flax
class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      __UpperCamelCase							=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	FlaxGPTJModelTester(self)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						int							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(lowercase_		,			lowercase_		,			lowercase_		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  lowercase_		,			lowercase_		,			lowercase_		,			lowercase_)
      @tooslow
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	GPTaTokenizer.from_pretrained('''gpt2'''		,			pad_token='''<|endoftext|>'''		,			padding_side='''left''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(['''Hello this is a long string''', '''Hey''']		,			return_tensors='''np'''		,			padding=lowercase_		,			truncation=lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[str]							=	FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	False
          SCREAMING_SNAKE_CASE_   :						str							=	model.config.eos_token_id

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jax.jit(model.generate)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	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(lowercase_		,			skip_special_tokens=lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	[
              '''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(lowercase_		,			lowercase_)
      @is_pt_flax_cross_test
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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_   :						Union[str, Any]							=	self._prepare_for_class(lowercase_		,			lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Any							=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

                  SCREAMING_SNAKE_CASE_   :						List[str]							=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,			lowercase_)
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	fx_state

                  with torch.no_grad():
                      SCREAMING_SNAKE_CASE_   :						Dict							=	pt_model(**lowercase_).to_tuple()

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

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

                  SCREAMING_SNAKE_CASE_   :						str							=	fx_model_loaded(**lowercase_).to_tuple()
                  self.assertEqual(
                      len(lowercase_)		,			len(lowercase_)		,			'''Output lengths differ between Flax and PyTorch''')
                  for fx_output_loaded, pt_output in zip(lowercase_		,			lowercase_):
                      self.assert_almost_equals(fx_output_loaded[:, -1]		,			pt_output[:, -1].numpy()		,			4e-2)
      @is_pt_flax_cross_test
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						List[str]							=	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(lowercase_		,			lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	{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_   :						Dict							=	getattr(lowercase_		,			lowercase_)

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

                  SCREAMING_SNAKE_CASE_   :						int							=	load_flax_weights_in_pytorch_model(lowercase_		,			fx_model.params)
                  SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	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(lowercase_):
                      SCREAMING_SNAKE_CASE_   :						int							=	0
                      SCREAMING_SNAKE_CASE_   :						Optional[int]							=	1
                      SCREAMING_SNAKE_CASE_   :						str							=	0
                      SCREAMING_SNAKE_CASE_   :						List[str]							=	1

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

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

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      fx_model.save_pretrained(lowercase_)
                      SCREAMING_SNAKE_CASE_   :						List[str]							=	pt_model_class.from_pretrained(lowercase_		,			from_flax=lowercase_)

                  with torch.no_grad():
                      SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	pt_model_loaded(**lowercase_).to_tuple()

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



      @tooslow
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_   :						Any							=	model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
              SCREAMING_SNAKE_CASE_   :						List[str]							=	model(np.ones((1, 1)))
              self.assertIsNotNone(lowercase_)


