






"""simple docstring"""



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   ,			__lowercase   ,			__lowercase=14   ,			__lowercase=7   ,			__lowercase=True   ,			__lowercase=True   ,			__lowercase=False   ,			__lowercase=True   ,			__lowercase=99   ,			__lowercase=32   ,			__lowercase=4   ,			__lowercase=4   ,			__lowercase=[[["global", "local"], 2]]   ,			__lowercase=37   ,			__lowercase="gelu"   ,			__lowercase=0.1   ,			__lowercase=0.1   ,			__lowercase=512   ,			__lowercase=7   ,			__lowercase=0.02   ,			):
     UpperCAmelCase__ 						=				parent
     UpperCAmelCase__ 						=				batch_size
     UpperCAmelCase__ 						=				seq_length
     UpperCAmelCase__ 						=				is_training
     UpperCAmelCase__ 						=				use_input_mask
     UpperCAmelCase__ 						=				use_token_type_ids
     UpperCAmelCase__ 						=				use_labels
     UpperCAmelCase__ 						=				vocab_size
     UpperCAmelCase__ 						=				hidden_size
     UpperCAmelCase__ 						=				num_hidden_layers
     UpperCAmelCase__ 						=				num_attention_heads
     UpperCAmelCase__ 						=				attention_types
     UpperCAmelCase__ 						=				intermediate_size
     UpperCAmelCase__ 						=				hidden_act
     UpperCAmelCase__ 						=				hidden_dropout_prob
     UpperCAmelCase__ 						=				attention_probs_dropout_prob
     UpperCAmelCase__ 						=				max_position_embeddings
     UpperCAmelCase__ 						=				window_size
     UpperCAmelCase__ 						=				initializer_range
     UpperCAmelCase__ 						=				None
     UpperCAmelCase__ 						=				vocab_size - 1
     UpperCAmelCase__ 						=				vocab_size - 1
     UpperCAmelCase__ 						=				vocab_size - 1


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size			)

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

     UpperCAmelCase__ 						=				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=_lowerCamelCase   ,			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 		A__      (      self			):
     UpperCAmelCase__ 						=				self.prepare_config_and_inputs()
     UpperCAmelCase__ 						=				config_and_inputs
     UpperCAmelCase__ 						=				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict


    def 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				20
     UpperCAmelCase__ 						=				model_class_name(_lowerCamelCase			)

     UpperCAmelCase__ 						=				model.init_cache(input_ids.shape[0]   ,			_lowerCamelCase			)
     UpperCAmelCase__ 						=				jnp.ones((input_ids.shape[0], max_decoder_length)   ,			dtype="""i4"""			)

     UpperCAmelCase__ 						=				jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1			)[None, :]   ,			(input_ids.shape[0], input_ids.shape[-1] - 1)			)
     UpperCAmelCase__ 						=				model(
         input_ids[:, :-1]   ,			attention_mask=_lowerCamelCase   ,			past_key_values=_lowerCamelCase   ,			position_ids=_lowerCamelCase   ,			)

     UpperCAmelCase__ 						=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,			dtype="""i4"""			)
     UpperCAmelCase__ 						=				model(
         input_ids[:, -1:]   ,			attention_mask=_lowerCamelCase   ,			past_key_values=outputs_cache.past_key_values   ,			position_ids=_lowerCamelCase   ,			)

     UpperCAmelCase__ 						=				model(_lowerCamelCase			)

     UpperCAmelCase__ 						=				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 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				20
     UpperCAmelCase__ 						=				model_class_name(_lowerCamelCase			)

     UpperCAmelCase__ 						=				jnp.concatenate(
         [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])			)]   ,			axis=-1   ,			)

     UpperCAmelCase__ 						=				model.init_cache(input_ids.shape[0]   ,			_lowerCamelCase			)
     UpperCAmelCase__ 						=				jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1			)[None, :]   ,			(input_ids.shape[0], input_ids.shape[-1] - 1)			)

     UpperCAmelCase__ 						=				model(
         input_ids[:, :-1]   ,			attention_mask=_lowerCamelCase   ,			past_key_values=_lowerCamelCase   ,			position_ids=_lowerCamelCase   ,			)
     UpperCAmelCase__ 						=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,			dtype="""i4"""			)
     UpperCAmelCase__ 						=				model(
         input_ids[:, -1:]   ,			past_key_values=outputs_cache.past_key_values   ,			attention_mask=_lowerCamelCase   ,			position_ids=_lowerCamelCase   ,			)

     UpperCAmelCase__ 						=				model(_lowerCamelCase   ,			attention_mask=_lowerCamelCase			)

     UpperCAmelCase__ 						=				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'''


    __lowercase    :   Any											=						(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
    __lowercase    :   Union[str, Any]											=						(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				FlaxGPTNeoModelTester(self			)


    def 		A__      (      self			):







     for model_class_name in self.all_model_classes:
      UpperCAmelCase__ 						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward(_lowerCamelCase   ,			_lowerCamelCase   ,			_lowerCamelCase   ,			_lowerCamelCase			)


    def 		A__      (      self			):







     for model_class_name in self.all_model_classes:
      UpperCAmelCase__ 						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward_with_attn_mask(
          _lowerCamelCase   ,			_lowerCamelCase   ,			_lowerCamelCase   ,			_lowerCamelCase			)


    @slow
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				GPTaTokenizer.from_pretrained("""gpt2"""   ,			pad_token="""<|endoftext|>"""   ,			padding_side="""left"""			)
     UpperCAmelCase__ 						=				tokenizer(["""Hello this is a long string""", """Hey"""]   ,			return_tensors="""np"""   ,			padding=_lowerCamelCase   ,			truncation=_lowerCamelCase			)

     UpperCAmelCase__ 						=				FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""			)
     UpperCAmelCase__ 						=				False
     UpperCAmelCase__ 						=				model.config.eos_token_id

     UpperCAmelCase__ 						=				jax.jit(model.generate			)

     UpperCAmelCase__ 						=				jit_generate(
         inputs["""input_ids"""]   ,			attention_mask=inputs["""attention_mask"""]   ,			pad_token_id=tokenizer.pad_token_id			).sequences

     UpperCAmelCase__ 						=				tokenizer.batch_decode(_lowerCamelCase   ,			skip_special_tokens=_lowerCamelCase			)

     UpperCAmelCase__ 						=				[
         '''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 		A__      (      self			):
     UpperCAmelCase__ 						=				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
       UpperCAmelCase__ 						=				self._prepare_for_class(_lowerCamelCase   ,			_lowerCamelCase			)
       UpperCAmelCase__ 						=				{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

       # load corresponding PyTorch class
       UpperCAmelCase__ 						=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
       UpperCAmelCase__ 						=				getattr(_lowerCamelCase   ,			_lowerCamelCase			)

       UpperCAmelCase__ 						=				pt_inputs['''input_ids'''].shape
       UpperCAmelCase__ 						=				np.random.randint(0   ,			seq_length - 1   ,			size=(batch_size,)			)
       for batch_idx, start_index in enumerate(_lowerCamelCase			):
        UpperCAmelCase__ 						=				0
        UpperCAmelCase__ 						=				1
        UpperCAmelCase__ 						=				0
        UpperCAmelCase__ 						=				1
       UpperCAmelCase__ 						=				pt_model_class(_lowerCamelCase			).eval()
       UpperCAmelCase__ 						=				model_class(_lowerCamelCase   ,			dtype=jnp.floataa			)

       UpperCAmelCase__ 						=				convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,			_lowerCamelCase			)
       UpperCAmelCase__ 						=				fx_state

       with torch.no_grad():
        UpperCAmelCase__ 						=				pt_model(**_lowerCamelCase			).to_tuple()

       UpperCAmelCase__ 						=				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			)
        UpperCAmelCase__ 						=				model_class.from_pretrained(_lowerCamelCase   ,			from_pt=_lowerCamelCase			)

       UpperCAmelCase__ 						=				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 		A__      (      self			):
     UpperCAmelCase__ 						=				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
       UpperCAmelCase__ 						=				self._prepare_for_class(_lowerCamelCase   ,			_lowerCamelCase			)
       UpperCAmelCase__ 						=				{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

       # load corresponding PyTorch class
       UpperCAmelCase__ 						=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
       UpperCAmelCase__ 						=				getattr(_lowerCamelCase   ,			_lowerCamelCase			)

       UpperCAmelCase__ 						=				pt_model_class(_lowerCamelCase			).eval()
       UpperCAmelCase__ 						=				model_class(_lowerCamelCase   ,			dtype=jnp.floataa			)

       UpperCAmelCase__ 						=				load_flax_weights_in_pytorch_model(_lowerCamelCase   ,			fx_model.params			)
       UpperCAmelCase__ 						=				pt_inputs['''input_ids'''].shape
       UpperCAmelCase__ 						=				np.random.randint(0   ,			seq_length - 1   ,			size=(batch_size,)			)
       for batch_idx, start_index in enumerate(_lowerCamelCase			):
        UpperCAmelCase__ 						=				0
        UpperCAmelCase__ 						=				1
        UpperCAmelCase__ 						=				0
        UpperCAmelCase__ 						=				1

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

       with torch.no_grad():
        UpperCAmelCase__ 						=				pt_model(**_lowerCamelCase			).to_tuple()

       UpperCAmelCase__ 						=				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			)
        UpperCAmelCase__ 						=				pt_model_class.from_pretrained(_lowerCamelCase   ,			from_flax=_lowerCamelCase			)

       with torch.no_grad():
        UpperCAmelCase__ 						=				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			)






    @slow
    def 		A__      (      self			):







     for model_class_name in self.all_model_classes:
      UpperCAmelCase__ 						=				model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""			)
      UpperCAmelCase__ 						=				model(np.ones((1, 1)			)			)
      self.assertIsNotNone(_lowerCamelCase			)









'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__ 							=    logging.get_logger(__name__)

snake_case__ 							=    {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}






class      UpperCamelCase_  (a__  ):







						"""simple docstring"""


						_lowerCAmelCase         =       'nystromformer'







						def __init__(  self   :							Optional[Any]				,			_lowerCamelCase   :							str=30000				,			_lowerCamelCase   :							Optional[Any]=768				,			_lowerCamelCase   :							Optional[Any]=12				,			_lowerCamelCase   :							str=12				,			_lowerCamelCase   :							List[str]=3072				,			_lowerCamelCase   :							Any="gelu_new"				,			_lowerCamelCase   :							List[Any]=0.1				,			_lowerCamelCase   :							List[str]=0.1				,			_lowerCamelCase   :							List[str]=510				,			_lowerCamelCase   :							Optional[Any]=2				,			_lowerCamelCase   :							List[Any]=64				,			_lowerCamelCase   :							str=64				,			_lowerCamelCase   :							str=65				,			_lowerCamelCase   :							List[Any]=False				,			_lowerCamelCase   :							List[Any]=0.02				,			_lowerCamelCase   :							List[Any]=1E-5				,			_lowerCamelCase   :							Dict=1				,			_lowerCamelCase   :							Tuple=0				,			_lowerCamelCase   :							List[str]=2				,			**_lowerCamelCase   :							List[str]				,			):





											"""simple docstring"""







											A_			:  List[Any]			=				vocab_size
											A_			:  Tuple			=				max_position_embeddings
											A_			:  Dict			=				hidden_size
											A_			:  Optional[Any]			=				num_hidden_layers
											A_			:  Dict			=				num_attention_heads
											A_			:  Tuple			=				intermediate_size
											A_			:  Dict			=				hidden_act
											A_			:  List[Any]			=				hidden_dropout_prob
											A_			:  str			=				attention_probs_dropout_prob
											A_			:  Optional[Any]			=				initializer_range
											A_			:  List[str]			=				type_vocab_size
											A_			:  List[Any]			=				segment_means_seq_len
											A_			:  Dict			=				num_landmarks
											A_			:  int			=				conv_kernel_size
											A_			:  Optional[Any]			=				inv_coeff_init_option
											A_			:  str			=				layer_norm_eps
											super().__init__(pad_token_id=_lowerCamelCase				,			bos_token_id=_lowerCamelCase				,			eos_token_id=_lowerCamelCase				,			**_lowerCamelCase     )



