code: 
  
  
  import tempfile
  import unittest
  
  import numpy as np
  
  import transformers
  from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
  
  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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel
  
  if is_torch_available():
     import torch
  
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Any	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       Dict=14	,      UpperCamelCase       :       Any=7	,      UpperCamelCase       :       Tuple=True	,      UpperCamelCase       :       Optional[Any]=True	,      UpperCamelCase       :       Any=False	,      UpperCamelCase       :       str=True	,      UpperCamelCase       :       List[str]=99	,      UpperCamelCase       :       Tuple=32	,      UpperCamelCase       :       Union[str, Any]=4	,      UpperCamelCase       :       Union[str, Any]=4	,      UpperCamelCase       :       Optional[int]=[[["global", "local"], 2]]	,      UpperCamelCase       :       Dict=37	,      UpperCamelCase       :       Any="gelu"	,      UpperCamelCase       :       str=0.1	,      UpperCamelCase       :       Optional[int]=0.1	,      UpperCamelCase       :       Optional[int]=5_12	,      UpperCamelCase       :       Optional[int]=7	,      UpperCamelCase       :       Optional[Any]=0.02	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			parent
              _lowercase					:				Dict           =			batch_size
              _lowercase					:				Optional[int]           =			seq_length
              _lowercase					:				Union[str, Any]           =			is_training
              _lowercase					:				List[Any]           =			use_input_mask
              _lowercase					:				List[Any]           =			use_token_type_ids
              _lowercase					:				int           =			use_labels
              _lowercase					:				Tuple           =			vocab_size
              _lowercase					:				Union[str, Any]           =			hidden_size
              _lowercase					:				Optional[Any]           =			num_hidden_layers
              _lowercase					:				Any           =			num_attention_heads
              _lowercase					:				str           =			attention_types
              _lowercase					:				Optional[Any]           =			intermediate_size
              _lowercase					:				Any           =			hidden_act
              _lowercase					:				Optional[Any]           =			hidden_dropout_prob
              _lowercase					:				Optional[Any]           =			attention_probs_dropout_prob
              _lowercase					:				Optional[int]           =			max_position_embeddings
              _lowercase					:				Optional[Any]           =			window_size
              _lowercase					:				Dict           =			initializer_range
              _lowercase					:				Optional[int]           =			None
              _lowercase					:				Tuple           =			vocab_size - 1
              _lowercase					:				Dict           =			vocab_size - 1
              _lowercase					:				Union[str, Any]           =			vocab_size - 1
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Union[str, Any]           =			ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size		)
  
              _lowercase					:				Union[str, Any]           =			None
              if self.use_input_mask:
                     _lowercase					:				List[Any]           =			random_attention_mask([self.batch_size, self.seq_length]		)
  
              _lowercase					:				int           =			GPTNeoConfig(
                  vocab_size=self.vocab_size	,      hidden_size=self.hidden_size	,      num_layers=self.num_hidden_layers	,      num_heads=self.num_attention_heads	,      max_position_embeddings=self.max_position_embeddings	,      use_cache=UpperCamelCase	,      bos_token_id=self.bos_token_id	,      eos_token_id=self.eos_token_id	,      pad_token_id=self.pad_token_id	,      window_size=self.window_size	,      attention_types=self.attention_types	,      )
  
              return (config, input_ids, input_mask)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				str           =			self.prepare_config_and_inputs()
              _lowercase       ,	_lowercase       ,	_lowercase					:				List[Any]           =			config_and_inputs
              _lowercase					:				Union[str, Any]           =			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
              return config, inputs_dict
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict	,      UpperCamelCase       :       Any	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       str	,      UpperCamelCase       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Dict           =			20
              _lowercase					:				List[Any]           =			model_class_name(UpperCamelCase		)
  
              _lowercase					:				int           =			model.init_cache(input_ids.shape[0]	,      UpperCamelCase		)
              _lowercase					:				Union[str, Any]           =			jnp.ones((input_ids.shape[0], max_decoder_length)	,      dtype='''i4'''		)
  
              _lowercase					:				Optional[Any]           =			jnp.broadcast_to(
                  jnp.arange(input_ids.shape[-1] - 1		)[None, :]	,      (input_ids.shape[0], input_ids.shape[-1] - 1)		)
              _lowercase					:				Union[str, Any]           =			model(
                  input_ids[:, :-1]	,      attention_mask=UpperCamelCase	,      past_key_values=UpperCamelCase	,      position_ids=UpperCamelCase	,      )
  
              _lowercase					:				str           =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,      dtype='''i4'''		)
              _lowercase					:				Optional[Any]           =			model(
                  input_ids[:, -1:]	,      attention_mask=UpperCamelCase	,      past_key_values=outputs_cache.past_key_values	,      position_ids=UpperCamelCase	,      )
  
              _lowercase					:				List[Any]           =			model(UpperCamelCase		)
  
              _lowercase					:				Tuple           =			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   lowerCAmelCase_   ( self       :       Optional[int]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int	,      UpperCamelCase       :       Dict	,      UpperCamelCase       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			20
              _lowercase					:				Any           =			model_class_name(UpperCamelCase		)
  
              _lowercase					:				List[Any]           =			jnp.concatenate(
                  [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])		)]	,      axis=-1	,      )
  
              _lowercase					:				Optional[Any]           =			model.init_cache(input_ids.shape[0]	,      UpperCamelCase		)
              _lowercase					:				Optional[Any]           =			jnp.broadcast_to(
                  jnp.arange(input_ids.shape[-1] - 1		)[None, :]	,      (input_ids.shape[0], input_ids.shape[-1] - 1)		)
  
              _lowercase					:				List[Any]           =			model(
                  input_ids[:, :-1]	,      attention_mask=UpperCamelCase	,      past_key_values=UpperCamelCase	,      position_ids=UpperCamelCase	,      )
              _lowercase					:				int           =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,      dtype='''i4'''		)
              _lowercase					:				Optional[int]           =			model(
                  input_ids[:, -1:]	,      past_key_values=outputs_cache.past_key_values	,      attention_mask=UpperCamelCase	,      position_ids=UpperCamelCase	,      )
  
              _lowercase					:				Any           =			model(UpperCamelCase	,      attention_mask=UpperCamelCase		)
  
              _lowercase					:				Optional[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}'		)
  
  
  
  
  @require_flax
  class 						UpperCAmelCase__   (      A_      ,						A_      ,						unittest.TestCase		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
       UpperCAmelCase_         =  (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			FlaxGPTNeoModelTester(self		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for model_class_name in self.all_model_classes:
                     _lowercase       ,	_lowercase       ,	_lowercase					:				str           =			self.model_tester.prepare_config_and_inputs()
                     self.model_tester.check_use_cache_forward(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for model_class_name in self.all_model_classes:
                     _lowercase       ,	_lowercase       ,	_lowercase					:				Optional[Any]           =			self.model_tester.prepare_config_and_inputs()
                     self.model_tester.check_use_cache_forward_with_attn_mask(
                         UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       @slow
       def   lowerCAmelCase_   ( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			GPTaTokenizer.from_pretrained('''gpt2'''	,      pad_token='''<|endoftext|>'''	,      padding_side='''left'''		)
              _lowercase					:				Dict           =			tokenizer(['''Hello this is a long string''', '''Hey''']	,      return_tensors='''np'''	,      padding=UpperCamelCase	,      truncation=UpperCamelCase		)
  
              _lowercase					:				List[str]           =			FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''		)
              _lowercase					:				str           =			False
              _lowercase					:				Tuple           =			model.config.eos_token_id
  
              _lowercase					:				int           =			jax.jit(model.generate		)
  
              _lowercase					:				int           =			jit_generate(
                  inputs['''input_ids''']	,      attention_mask=inputs['''attention_mask''']	,      pad_token_id=tokenizer.pad_token_id		).sequences
  
              _lowercase					:				int           =			tokenizer.batch_decode(UpperCamelCase	,      skip_special_tokens=UpperCamelCase		)
  
              _lowercase					:				Any           =			[
                  '''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(UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       @is_pt_flax_cross_test
       def   lowerCAmelCase_   ( self       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase       ,	_lowercase					:				Tuple           =			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
                            _lowercase					:				Any           =			self._prepare_for_class(UpperCamelCase	,      UpperCamelCase		)
                            _lowercase					:				List[str]           =			{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}
  
                            # load corresponding PyTorch class
                            _lowercase					:				int           =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
                            _lowercase					:				Optional[int]           =			getattr(UpperCamelCase	,      UpperCamelCase		)
  
                            _lowercase       ,	_lowercase					:				Dict           =			pt_inputs['''input_ids'''].shape
                            _lowercase					:				Tuple           =			np.random.randint(0	,      seq_length - 1	,      size=(batch_size,)		)
                            for batch_idx, start_index in enumerate(UpperCamelCase		):
                                   _lowercase					:				Optional[int]           =			0
                                   _lowercase					:				Optional[Any]           =			1
                                   _lowercase					:				Tuple           =			0
                                   _lowercase					:				Union[str, Any]           =			1
                            _lowercase					:				Dict           =			pt_model_class(UpperCamelCase		).eval()
                            _lowercase					:				Any           =			model_class(UpperCamelCase	,      dtype=jnp.floataa		)
  
                            _lowercase					:				Dict           =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,      UpperCamelCase		)
                            _lowercase					:				int           =			fx_state
  
                            with torch.no_grad():
                                   _lowercase					:				Optional[Any]           =			pt_model(**UpperCamelCase		).to_tuple()
  
                            _lowercase					:				Union[str, Any]           =			fx_model(**UpperCamelCase		).to_tuple()
                            self.assertEqual(len(UpperCamelCase		)	,      len(UpperCamelCase		)	,      '''Output lengths differ between Flax and PyTorch'''		)
                            for fx_output, pt_output in zip(UpperCamelCase	,      UpperCamelCase		):
                                   self.assert_almost_equals(fx_output[:, -1]	,      pt_output[:, -1].numpy()	,      4E-2		)
  
                            with tempfile.TemporaryDirectory() as tmpdirname:
                                   pt_model.save_pretrained(UpperCamelCase		)
                                   _lowercase					:				Tuple           =			model_class.from_pretrained(UpperCamelCase	,      from_pt=UpperCamelCase		)
  
                            _lowercase					:				List[Any]           =			fx_model_loaded(**UpperCamelCase		).to_tuple()
                            self.assertEqual(
                                len(UpperCamelCase		)	,      len(UpperCamelCase		)	,      '''Output lengths differ between Flax and PyTorch'''		)
                            for fx_output_loaded, pt_output in zip(UpperCamelCase	,      UpperCamelCase		):
                                   self.assert_almost_equals(fx_output_loaded[:, -1]	,      pt_output[:, -1].numpy()	,      4E-2		)
  
  
  
  
       @is_pt_flax_cross_test
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase       ,	_lowercase					:				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
                            _lowercase					:				List[str]           =			self._prepare_for_class(UpperCamelCase	,      UpperCamelCase		)
                            _lowercase					:				Optional[Any]           =			{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}
  
                            # load corresponding PyTorch class
                            _lowercase					:				Optional[Any]           =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
                            _lowercase					:				Dict           =			getattr(UpperCamelCase	,      UpperCamelCase		)
  
                            _lowercase					:				Optional[int]           =			pt_model_class(UpperCamelCase		).eval()
                            _lowercase					:				Dict           =			model_class(UpperCamelCase	,      dtype=jnp.floataa		)
  
                            _lowercase					:				Tuple           =			load_flax_weights_in_pytorch_model(UpperCamelCase	,      fx_model.params		)
                            _lowercase       ,	_lowercase					:				Optional[int]           =			pt_inputs['''input_ids'''].shape
                            _lowercase					:				Dict           =			np.random.randint(0	,      seq_length - 1	,      size=(batch_size,)		)
                            for batch_idx, start_index in enumerate(UpperCamelCase		):
                                   _lowercase					:				List[Any]           =			0
                                   _lowercase					:				List[str]           =			1
                                   _lowercase					:				Tuple           =			0
                                   _lowercase					:				str           =			1
  
                            # make sure weights are tied in PyTorch
                            pt_model.tie_weights()
  
                            with torch.no_grad():
                                   _lowercase					:				Optional[Any]           =			pt_model(**UpperCamelCase		).to_tuple()
  
                            _lowercase					:				Dict           =			fx_model(**UpperCamelCase		).to_tuple()
                            self.assertEqual(len(UpperCamelCase		)	,      len(UpperCamelCase		)	,      '''Output lengths differ between Flax and PyTorch'''		)
                            for fx_output, pt_output in zip(UpperCamelCase	,      UpperCamelCase		):
                                   self.assert_almost_equals(fx_output[:, -1]	,      pt_output[:, -1].numpy()	,      4E-2		)
  
                            with tempfile.TemporaryDirectory() as tmpdirname:
                                   fx_model.save_pretrained(UpperCamelCase		)
                                   _lowercase					:				List[str]           =			pt_model_class.from_pretrained(UpperCamelCase	,      from_flax=UpperCamelCase		)
  
                            with torch.no_grad():
                                   _lowercase					:				List[Any]           =			pt_model_loaded(**UpperCamelCase		).to_tuple()
  
                            self.assertEqual(
                                len(UpperCamelCase		)	,      len(UpperCamelCase		)	,      '''Output lengths differ between Flax and PyTorch'''		)
                            for fx_output, pt_output in zip(UpperCamelCase	,      UpperCamelCase		):
                                   self.assert_almost_equals(fx_output[:, -1]	,      pt_output[:, -1].numpy()	,      4E-2		)
  
  
  
  
       @slow
       def   lowerCAmelCase_   ( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for model_class_name in self.all_model_classes:
                     _lowercase					:				List[str]           =			model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''		)
                     _lowercase					:				Tuple           =			model(np.ones((1, 1)		)		)
                     self.assertIsNotNone(UpperCamelCase		)
style_context: 
  
  
  # limitations under the License.
  
  
  from typing import Optional, Tuple, Union
  
  import torch
  
  from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              self.register_modules(unet=UpperCamelCase	,      scheduler=UpperCamelCase		)
  
  
  
  
       @torch.no_grad()
       def __call__( self       :       Any	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       Optional[torch.Generator] = None	,      UpperCamelCase       :       int = 50	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      **UpperCamelCase       :       int	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			torch.randn(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,      generator=UpperCamelCase	,      )
              _lowercase					:				Dict           =			image.to(self.device		)
  
              # set step values
              self.scheduler.set_timesteps(UpperCamelCase		)
  
              for t in self.progress_bar(self.scheduler.timesteps		):
                     # 1. predict noise model_output
                     _lowercase					:				Dict           =			self.unet(UpperCamelCase	,      UpperCamelCase		).sample
  
                     # 2. predict previous mean of image x_t-1 and add variance depending on eta
                     # eta corresponds to η in paper and should be between [0, 1]
                     # do x_t -> x_t-1
                     _lowercase					:				Tuple           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample
  
              _lowercase					:				Optional[int]           =			(image / 2 + 0.5).clamp(0	,      1		)
              _lowercase					:				int           =			image.cpu().permute(0	,      2	,      3	,      1		).numpy()
              if output_type == "pil":
                     _lowercase					:				List[Any]           =			self.numpy_to_pil(UpperCamelCase		)
  
              if not return_dict:
                     return (image,), "This is a local test"
  
              return ImagePipelineOutput(images=UpperCamelCase		), "This is a local test"