

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 		__magic_name__    :

   '''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,					)							->					List[Any]:






     """simple docstring"""






     a__						=parent
     a__						=batch_size
     a__						=seq_length
     a__						=is_training
     a__						=use_input_mask
     a__						=use_token_type_ids
     a__						=use_labels
     a__						=vocab_size
     a__						=hidden_size
     a__						=num_hidden_layers
     a__						=num_attention_heads
     a__						=attention_types
     a__						=intermediate_size
     a__						=hidden_act
     a__						=hidden_dropout_prob
     a__						=attention_probs_dropout_prob
     a__						=max_position_embeddings
     a__						=window_size
     a__						=initializer_range
     a__						=None
     a__						=vocab_size - 1
     a__						=vocab_size - 1
     a__						=vocab_size - 1



   def 				_UpperCAmelCase				(       self )							->					Union[str, Any]:






     """simple docstring"""






     a__						=ids_tensor([self.batch_size, self.seq_length],					self.vocab_size )

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

     a__						=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=lowercase_,					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 				_UpperCAmelCase				(       self )							->					List[Any]:






     """simple docstring"""






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



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					str:






     """simple docstring"""






     a__						=20
     a__						=model_class_name(lowercase_ )

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

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

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

     a__						=model(lowercase_ )

     a__						=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 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					int:






     """simple docstring"""






     a__						=20
     a__						=model_class_name(lowercase_ )

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

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

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

     a__						=model(lowercase_,					attention_mask=lowercase_ )

     a__						=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 		__magic_name__    (							lowerCamelCase__	, lowerCamelCase__	, unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__				:			int												=     (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
   lowerCamelCase__				:			int												=     (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



   def 				_UpperCAmelCase				(       self )							->					Any:






     """simple docstring"""






     a__						=FlaxGPTNeoModelTester(self )



   def 				_UpperCAmelCase				(       self )							->					Optional[Any]:






     """simple docstring"""






     for model_class_name in self.all_model_classes:
       a__,   a__,   a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(lowercase_,					lowercase_,					lowercase_,					lowercase_ )



   def 				_UpperCAmelCase				(       self )							->					Union[str, Any]:






     """simple docstring"""






     for model_class_name in self.all_model_classes:
       a__,   a__,   a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           lowercase_,					lowercase_,					lowercase_,					lowercase_ )



   @slow
   def 				_UpperCAmelCase				(       self )							->					int:






     """simple docstring"""






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

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

     a__						=jax.jit(model.generate )

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

     a__						=tokenizer.batch_decode(lowercase_,					skip_special_tokens=lowercase_ )

     a__						=[
         '''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 				_UpperCAmelCase				(       self )							->					Any:






     """simple docstring"""






     a__,   a__						=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
         a__						=self._prepare_for_class(lowercase_,					lowercase_ )
         a__						={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

         a__						=convert_pytorch_state_dict_to_flax(pt_model.state_dict(),					lowercase_ )
         a__						=fx_state

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

         a__						=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_ )
           a__						=model_class.from_pretrained(lowercase_,					from_pt=lowercase_ )

         a__						=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 				_UpperCAmelCase				(       self )							->					Any:






     """simple docstring"""






     a__,   a__						=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
         a__						=self._prepare_for_class(lowercase_,					lowercase_ )
         a__						={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

         a__						=pt_model_class(lowercase_ ).eval()
         a__						=model_class(lowercase_,					dtype=jnp.floataa )

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

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

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

         a__						=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_ )
           a__						=pt_model_class.from_pretrained(lowercase_,					from_flax=lowercase_ )

         with torch.no_grad():
           a__						=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 )



   @slow
   def 				_UpperCAmelCase				(       self )							->					Any:






     """simple docstring"""






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



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


lowerCamelCase     =      get_logger(__name__)




def     UpperCAmelCase__       (    _A   :				str			,   _A   :				List[dict]			,   _A   :				list			,   _A   :				list			,   _A   :				int			,   _A   :				GradScaler = None			,   ):





  '''simple docstring'''







  for i, state in enumerate(_A							):
    a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Model weights saved in {output_model_file}"""							)
  # Optimizer states
  for i, opt in enumerate(_A							):
    a__						=opt.state_dict()
    a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__						=scheduler.state_dict()
    a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Scheduler state saved in {output_scheduler_file}"""							)
  # GradScaler state
  if scaler is not None:
    a__						=scaler.state_dict()
    a__						=os.path.join(_A			,   _A							)
    torch.save(_A			,   _A							)
    logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
  # Random number generator states
  a__						={}
  a__						=F"""{RNG_STATE_NAME}_{process_index}.pkl"""
  a__						=random.getstate()
  a__						=np.random.get_state()
  a__						=torch.get_rng_state()
  if is_xpu_available():
    a__						=torch.xpu.get_rng_state_all()
  else:
    a__						=torch.cuda.get_rng_state_all()
  if is_tpu_available():
    a__						=xm.get_rng_state()
  a__						=os.path.join(_A			,   _A							)
  torch.save(_A			,   _A							)
  logger.info(F"""Random states saved in {output_states_file}"""							)
  return output_dir




def     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				int			,   _A   :				int			,   _A   :				Union[str, Any]			,   _A   :				Tuple			,   _A   :				Optional[int]=None			,   _A   :				List[Any]=None			,   **_A   :				Dict			,   ):





  '''simple docstring'''







  if map_location not in [None, "cpu", "on_device"]:
    raise TypeError(
        '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
  if map_location is None:
    a__						='''cpu'''
  elif map_location == "on_device":
    a__						=PartialState().device
  # Model states
  for i, model in enumerate(_A							):
    a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    models[i].load_state_dict(torch.load(_A			,   map_location=_A							)			,   **_A							)
  logger.info('''All model weights loaded successfully'''							)

  # Optimizer states
  for i, opt in enumerate(_A							):
    a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    a__						=torch.load(_A			,   map_location=_A							)
    optimizers[i].load_state_dict(_A							)
  logger.info('''All optimizer states loaded successfully'''							)

  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    scheduler.load_state_dict(torch.load(_A							)							)
  logger.info('''All scheduler states loaded successfully'''							)

  # GradScaler state
  if scaler is not None:
    a__						=os.path.join(_A			,   _A							)
    scaler.load_state_dict(torch.load(_A							)							)
    logger.info('''GradScaler state loaded successfully'''							)

  # Random states
  try:
    a__						=torch.load(os.path.join(_A			,   F"""{RNG_STATE_NAME}_{process_index}.pkl"""							)							)
    random.setstate(states['''random_state''']							)
    np.random.set_state(states['''numpy_random_seed''']							)
    torch.set_rng_state(states['''torch_manual_seed''']							)
    if is_xpu_available():
      torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
    else:
      torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
    if is_tpu_available():
      xm.set_rng_state(states['''xm_seed''']							)
    logger.info('''All random states loaded successfully'''							)
  except Exception:
    logger.info('''Could not load random states'''							)




def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				Any			,   _A   :				int = 0							):





  '''simple docstring'''







  a__						=Path(_A							) / F"""custom_checkpoint_{index}.pkl"""
  logger.info(F"""Saving the state of {get_pretty_name(_A							)} to {save_location}"""							)
  torch.save(obj.state_dict()			,   _A							)




def     UpperCAmelCase__       (    _A   :				int			,   _A   :				Any			,   _A   :				int = 0							):





  '''simple docstring'''







  a__						=F"""{path}/custom_checkpoint_{index}.pkl"""
  logger.info(F"""Loading the state of {get_pretty_name(_A							)} from {load_location}"""							)
  obj.load_state_dict(torch.load(_A			,   map_location='''cpu'''							)							)
