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       snake_case					:




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		parent
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		seq_length
       SCREAMING_SNAKE_CASE__				:				str			=		is_training
       SCREAMING_SNAKE_CASE__				:				Any			=		use_input_mask
       SCREAMING_SNAKE_CASE__				:				List[str]			=		use_token_type_ids
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		use_labels
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		vocab_size
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		hidden_size
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		rotary_dim
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		num_hidden_layers
       SCREAMING_SNAKE_CASE__				:				str			=		num_attention_heads
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		intermediate_size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		hidden_act
       SCREAMING_SNAKE_CASE__				:				str			=		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__				:				List[str]			=		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		max_position_embeddings
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		initializer_range
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		vocab_size - 1
       SCREAMING_SNAKE_CASE__				:				Any			=		vocab_size - 1
       SCREAMING_SNAKE_CASE__				:				str			=		vocab_size - 1




  def 	__lowercase(      self     :	Optional[Any]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size				)

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

       SCREAMING_SNAKE_CASE__				:				List[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=a_     , 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 	__lowercase(      self     :	List[Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		config_and_inputs
       SCREAMING_SNAKE_CASE__				:				str			=		{'input_ids': input_ids, 'attention_mask': attention_mask}
       return config, inputs_dict




  def 	__lowercase(      self     :	Tuple     , a_     :	List[Any]     , a_     :	Any     , a_     :	Any     , a_     :	Optional[Any]				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		20
       SCREAMING_SNAKE_CASE__				:				int			=		model_class_name(a_				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		model.init_cache(input_ids.shape[0]     , a_				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		jnp.ones((input_ids.shape[0], max_decoder_length)     , dtype='i4'				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1				)[None, :]     , (input_ids.shape[0], input_ids.shape[-1] - 1)				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(
           input_ids[:, :-1]     , attention_mask=a_     , past_key_values=a_     , position_ids=a_     , )

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

       SCREAMING_SNAKE_CASE__				:				List[str]			=		model(a_				)

       SCREAMING_SNAKE_CASE__				:				Union[str, 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}'''				)




  def 	__lowercase(      self     :	Tuple     , a_     :	Dict     , a_     :	str     , a_     :	Dict     , a_     :	List[Any]				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		20
       SCREAMING_SNAKE_CASE__				:				Tuple			=		model_class_name(a_				)

       SCREAMING_SNAKE_CASE__				:				List[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]     , a_				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1				)[None, :]     , (input_ids.shape[0], input_ids.shape[-1] - 1)				)

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

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(a_     , attention_mask=a_				)

       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       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
  lowercase_			=     (FlaxGPTJForCausalLM,) if is_flax_available() else ()




  def 	__lowercase(      self     :	List[Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		FlaxGPTJModelTester(self				)




  def 	__lowercase(      self     :	int				)->       str:




       """simple docstring"""
       for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward(a_     , a_     , a_     , a_				)




  def 	__lowercase(      self     :	Dict				)->       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(
                a_     , a_     , a_     , a_				)




  @tooslow
  def 	__lowercase(      self     :	Any				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		GPTaTokenizer.from_pretrained('gpt2'     , pad_token='<|endoftext|>'     , padding_side='left'				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		tokenizer(['Hello this is a long string', 'Hey']     , return_tensors='np'     , padding=a_     , truncation=a_				)

       SCREAMING_SNAKE_CASE__				:				Any			=		FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		False
       SCREAMING_SNAKE_CASE__				:				Any			=		model.config.eos_token_id

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		jax.jit(model.generate				)

       SCREAMING_SNAKE_CASE__				:				Any			=		jit_generate(
           inputs['input_ids']     , attention_mask=inputs['attention_mask']     , pad_token_id=tokenizer.pad_token_id				).sequences

       SCREAMING_SNAKE_CASE__				:				Any			=		tokenizer.batch_decode(a_     , skip_special_tokens=a_				)

       SCREAMING_SNAKE_CASE__				:				List[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(a_     , a_				)




  @is_pt_flax_cross_test
  def 	__lowercase(      self     :	Optional[int]				)->       Tuple:




       """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__				:				Any			=		self._prepare_for_class(a_     , a_				)
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

                 # load corresponding PyTorch class
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		getattr(a_     , a_				)

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

                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()     , a_				)
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		fx_state

                 with torch.no_grad():
                      SCREAMING_SNAKE_CASE__				:				List[Any]			=		pt_model(**a_				).to_tuple()

                 SCREAMING_SNAKE_CASE__				:				Dict			=		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_				)
                      SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model_class.from_pretrained(a_     , from_pt=a_				)

                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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 	__lowercase(      self     :	int				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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__				:				List[Any]			=		self._prepare_for_class(a_     , a_				)
                 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__				:				List[str]			=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		getattr(a_     , a_				)

                 SCREAMING_SNAKE_CASE__				:				Any			=		pt_model_class(a_				).eval()
                 SCREAMING_SNAKE_CASE__				:				str			=		model_class(a_     , dtype=jnp.floataa				)

                 SCREAMING_SNAKE_CASE__				:				Dict			=		load_flax_weights_in_pytorch_model(a_     , fx_model.params				)
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		pt_inputs['input_ids'].shape
                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		np.random.randint(0     , seq_length - 1     , size=(batch_size,)				)
                 for batch_idx, start_index in enumerate(a_				):
                      SCREAMING_SNAKE_CASE__				:				Tuple			=		0
                      SCREAMING_SNAKE_CASE__				:				Optional[int]			=		1
                      SCREAMING_SNAKE_CASE__				:				Any			=		0
                      SCREAMING_SNAKE_CASE__				:				List[Any]			=		1

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

                 with torch.no_grad():
                      SCREAMING_SNAKE_CASE__				:				str			=		pt_model(**a_				).to_tuple()

                 SCREAMING_SNAKE_CASE__				:				Any			=		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_				)
                      SCREAMING_SNAKE_CASE__				:				int			=		pt_model_class.from_pretrained(a_     , from_flax=a_				)

                 with torch.no_grad():
                      SCREAMING_SNAKE_CASE__				:				Any			=		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				)




  @tooslow
  def 	__lowercase(      self     :	str				)->       Union[str, Any]:




       """simple docstring"""
       for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		model_class_name.from_pretrained('EleutherAI/gpt-j-6B'				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		model(np.ones((1, 1)				)				)
            self.assertIsNotNone(a_				)




import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
      from deepspeed import DeepSpeedEngine

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







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if is_torch_version('<'     ,  '2.0.0' ) or not hasattr(lowercase__     ,  '_dynamo' ):
          return False
     return isinstance(lowercase__     ,  torch._dynamo.eval_frame.OptimizedModule )







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			bool = True ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     SCREAMING_SNAKE_CASE__				:				Dict			=		is_compiled_module(lowercase__ )
     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		model
          SCREAMING_SNAKE_CASE__				:				int			=		model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(lowercase__     ,  lowercase__ ):
          SCREAMING_SNAKE_CASE__				:				Any			=		model.module

     if not keep_fpaa_wrapper:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		getattr(lowercase__     ,  'forward' )
          SCREAMING_SNAKE_CASE__				:				str			=		model.__dict__.pop('_original_forward'     ,  lowercase__ )
          if original_forward is not None:
               while hasattr(lowercase__     ,  '__wrapped__' ):
                    SCREAMING_SNAKE_CASE__				:				Dict			=		forward.__wrapped__
                    if forward == original_forward:
                         break
               SCREAMING_SNAKE_CASE__				:				Dict			=		forward
          if getattr(lowercase__     ,  '_converted_to_transformer_engine'     ,  lowercase__ ):
               convert_model(lowercase__     ,  to_transformer_engine=lowercase__ )

     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		model
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		compiled_model

     return model







def     _a				(      ):







     '''simple docstring'''


     PartialState().wait_for_everyone()







def     _a				(      lowercase__  :			str     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(lowercase__     ,  lowercase__ )
     elif PartialState().local_process_index == 0:
          torch.save(lowercase__     ,  lowercase__ )







@contextmanager
def     _a				(      **lowercase__  :			str ):







     '''simple docstring'''


     for key, value in kwargs.items():
          SCREAMING_SNAKE_CASE__				:				int			=		str(lowercase__ )

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if not hasattr(lowercase__     ,  '__qualname__' ) and not hasattr(lowercase__     ,  '__name__' ):
          SCREAMING_SNAKE_CASE__				:				Any			=		getattr(lowercase__     ,  '__class__'     ,  lowercase__ )
     if hasattr(lowercase__     ,  '__qualname__' ):
          return obj.__qualname__
     if hasattr(lowercase__     ,  '__name__' ):
          return obj.__name__
     return str(lowercase__ )







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			List[Any] ):







     '''simple docstring'''


     for key, value in source.items():
          if isinstance(lowercase__     ,  lowercase__ ):
               SCREAMING_SNAKE_CASE__				:				List[str]			=		destination.setdefault(lowercase__     ,  {} )
               merge_dicts(lowercase__     ,  lowercase__ )
          else:
               SCREAMING_SNAKE_CASE__				:				List[Any]			=		value

     return destination







def     _a				(      lowercase__  :			int = None ):







     '''simple docstring'''


     if port is None:
          SCREAMING_SNAKE_CASE__				:				int			=		2_95_00
     with socket.socket(socket.AF_INET     ,  socket.SOCK_STREAM ) as s:
          return s.connect_ex(('localhost', port) ) == 0



