




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 			A			:
     """simple docstring"""

     def __init__(    self  :   Union[str, Any]	,  A__  :   List[Any]	,  A__  :   Union[str, Any]=1_4	,  A__  :   Optional[int]=7	,  A__  :   Tuple=True	,  A__  :   int=True	,  A__  :   List[Any]=False	,  A__  :   List[Any]=True	,  A__  :   List[Any]=9_9	,  A__  :   Dict=3_2	,  A__  :   Tuple=4	,  A__  :   str=4	,  A__  :   int=[[["global", "local"], 2]]	,  A__  :   Optional[Any]=3_7	,  A__  :   Optional[int]="gelu"	,  A__  :   Optional[Any]=0.1	,  A__  :   Any=0.1	,  A__  :   List[Any]=5_1_2	,  A__  :   List[Any]=7	,  A__  :   Dict=0.02	,  )				->       Union[str, Any]:
           lowerCamelCase__        =				parent
           lowerCamelCase__        =				batch_size
           lowerCamelCase__        =				seq_length
           lowerCamelCase__        =				is_training
           lowerCamelCase__        =				use_input_mask
           lowerCamelCase__        =				use_token_type_ids
           lowerCamelCase__        =				use_labels
           lowerCamelCase__        =				vocab_size
           lowerCamelCase__        =				hidden_size
           lowerCamelCase__        =				num_hidden_layers
           lowerCamelCase__        =				num_attention_heads
           lowerCamelCase__        =				attention_types
           lowerCamelCase__        =				intermediate_size
           lowerCamelCase__        =				hidden_act
           lowerCamelCase__        =				hidden_dropout_prob
           lowerCamelCase__        =				attention_probs_dropout_prob
           lowerCamelCase__        =				max_position_embeddings
           lowerCamelCase__        =				window_size
           lowerCamelCase__        =				initializer_range
           lowerCamelCase__        =				None
           lowerCamelCase__        =				vocab_size - 1
           lowerCamelCase__        =				vocab_size - 1
           lowerCamelCase__        =				vocab_size - 1




     def  __snake_case   (    self  :   Optional[Any]			)				->       List[str]:
           lowerCamelCase__        =				ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size			)

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

           lowerCamelCase__        =				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=A__	,  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  __snake_case   (    self  :   List[Any]			)				->       str:
           lowerCamelCase__        =				self.prepare_config_and_inputs()
           lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				config_and_inputs
           lowerCamelCase__        =				{'input_ids': input_ids, 'attention_mask': attention_mask}
           return config, inputs_dict




     def  __snake_case   (    self  :   Any	,  A__  :   int	,  A__  :   List[Any]	,  A__  :   Optional[Any]	,  A__  :   List[str]			)				->       Optional[Any]:
           lowerCamelCase__        =				2_0
           lowerCamelCase__        =				model_class_name(A__			)

           lowerCamelCase__        =				model.init_cache(input_ids.shape[0]	,  A__			)
           lowerCamelCase__        =				jnp.ones((input_ids.shape[0], max_decoder_length)	,  dtype='i4'			)

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

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

           lowerCamelCase__        =				model(A__			)

           lowerCamelCase__        =				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  __snake_case   (    self  :   List[Any]	,  A__  :   Any	,  A__  :   Optional[Any]	,  A__  :   List[str]	,  A__  :   Dict			)				->       Any:
           lowerCamelCase__        =				2_0
           lowerCamelCase__        =				model_class_name(A__			)

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

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

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

           lowerCamelCase__        =				model(A__	,  attention_mask=A__			)

           lowerCamelCase__        =				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 			A			(		_lowerCAmelCase			,						_lowerCAmelCase			,						unittest.TestCase		):
     """simple docstring"""

     _snake_case:	str     =(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
     _snake_case:	Tuple     =(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




     def  __snake_case   (    self  :   int			)				->       Dict:
           lowerCamelCase__        =				FlaxGPTNeoModelTester(self			)




     def  __snake_case   (    self  :   List[Any]			)				->       List[str]:
           for model_class_name in self.all_model_classes:
                 lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(A__	,  A__	,  A__	,  A__			)




     def  __snake_case   (    self  :   List[Any]			)				->       Optional[int]:
           for model_class_name in self.all_model_classes:
                 lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     A__	,  A__	,  A__	,  A__			)




     @slow
     def  __snake_case   (    self  :   Optional[int]			)				->       str:
           lowerCamelCase__        =				GPTaTokenizer.from_pretrained('gpt2'	,  pad_token='<|endoftext|>'	,  padding_side='left'			)
           lowerCamelCase__        =				tokenizer(['Hello this is a long string', 'Hey']	,  return_tensors='np'	,  padding=A__	,  truncation=A__			)

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

           lowerCamelCase__        =				jax.jit(model.generate			)

           lowerCamelCase__        =				jit_generate(
               inputs['input_ids']	,  attention_mask=inputs['attention_mask']	,  pad_token_id=tokenizer.pad_token_id			).sequences

           lowerCamelCase__        =				tokenizer.batch_decode(A__	,  skip_special_tokens=A__			)

           lowerCamelCase__        =				[
               '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(A__	,  A__			)




     @is_pt_flax_cross_test
     def  __snake_case   (    self  :   Any			)				->       Optional[int]:
           lowerCamelCase__  ,		lowerCamelCase__        =				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
                       lowerCamelCase__        =				self._prepare_for_class(A__	,  A__			)
                       lowerCamelCase__        =				{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

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

                       lowerCamelCase__        =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,  A__			)
                       lowerCamelCase__        =				fx_state

                       with torch.no_grad():
                             lowerCamelCase__        =				pt_model(**A__			).to_tuple()

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

                       with tempfile.TemporaryDirectory() as tmpdirname:
                             pt_model.save_pretrained(A__			)
                             lowerCamelCase__        =				model_class.from_pretrained(A__	,  from_pt=A__			)

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




     @is_pt_flax_cross_test
     def  __snake_case   (    self  :   Union[str, Any]			)				->       List[str]:
           lowerCamelCase__  ,		lowerCamelCase__        =				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
                       lowerCamelCase__        =				self._prepare_for_class(A__	,  A__			)
                       lowerCamelCase__        =				{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

                       lowerCamelCase__        =				pt_model_class(A__			).eval()
                       lowerCamelCase__        =				model_class(A__	,  dtype=jnp.floataa			)

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

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

                       with torch.no_grad():
                             lowerCamelCase__        =				pt_model(**A__			).to_tuple()

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

                       with tempfile.TemporaryDirectory() as tmpdirname:
                             fx_model.save_pretrained(A__			)
                             lowerCamelCase__        =				pt_model_class.from_pretrained(A__	,  from_flax=A__			)

                       with torch.no_grad():
                             lowerCamelCase__        =				pt_model_loaded(**A__			).to_tuple()

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




     @slow
     def  __snake_case   (    self  :   List[Any]			)				->       Tuple:
           for model_class_name in self.all_model_classes:
                 lowerCamelCase__        =				model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'			)
                 lowerCamelCase__        =				model(np.ones((1, 1)			)			)
                 self.assertIsNotNone(A__			)





from ..utils import DummyObject, requires_backends






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   Dict	,  *A__  :   Optional[int]	,  **A__  :   Dict			)				->       Union[str, Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[int]	,  *A__  :   Dict	,  **A__  :   List[str]			)				->       Union[str, Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[Any]	,  *A__  :   Dict	,  **A__  :   List[Any]			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   List[str]	,  *A__  :   str	,  **A__  :   int			)				->       List[Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   str	,  *A__  :   Any	,  **A__  :   Dict			)				->       Optional[Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   List[str]	,  *A__  :   Optional[Any]	,  **A__  :   int			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)