
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)

def 		UpperCamelCase						(   lowercase_			,			lowercase_=False )		->						Dict:


  '''simple docstring'''






  try:
    lowercase__	: Any     		=   os.environ[key]
  except KeyError:
    # KEY isn't set, default to `default`.
    lowercase__	: str     		=   default
  else:
    # KEY is set, convert it to True or False.
    try:
      lowercase__	: Union[str, Any]     		=   strtobool(lowercase_ )
    except ValueError:
      # More values are supported, but let's keep the message simple.
      raise ValueError(F'If set, {key} must be yes or no.' )
  return _value


lowerCamelCase__     :		Union[str, Any]        = parse_flag_from_env("""RUN_SLOW""", default=False)

def 		UpperCamelCase						(   lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  return unittest.skip("""Test was skipped""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						List[str]:


  '''simple docstring'''






  return unittest.skipUnless(_run_slow_tests			,			"""test is slow""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Tuple:


  '''simple docstring'''






  return unittest.skipUnless(not torch.cuda.is_available()			,			"""test requires only a CPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  return unittest.skipUnless(torch.cuda.is_available()			,			"""test requires a GPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						List[str]:


  '''simple docstring'''






  return unittest.skipUnless(is_xpu_available()			,			"""test requires a XPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  return unittest.skipUnless(is_mps_available()			,			"""test requires a `mps` backend support in `torch`""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  return unittest.skipUnless(
      is_transformers_available() and is_datasets_available()			,			"""test requires the Hugging Face suite""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  return unittest.skipUnless(is_bnb_available()			,			"""test requires the bitsandbytes library""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  return unittest.skipUnless(is_tpu_available()			,			"""test requires TPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  return unittest.skipUnless(torch.cuda.device_count() == 1			,			"""test requires a GPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  return unittest.skipUnless(torch.xpu.device_count() == 1			,			"""test requires a XPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  return unittest.skipUnless(torch.cuda.device_count() > 1			,			"""test requires multiple GPUs""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  return unittest.skipUnless(torch.xpu.device_count() > 1			,			"""test requires multiple XPUs""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  return unittest.skipUnless(is_safetensors_available()			,			"""test requires safetensors""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  return unittest.skipUnless(is_deepspeed_available()			,			"""test requires DeepSpeed""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Tuple:


  '''simple docstring'''






  return unittest.skipUnless(is_torch_version(""">="""			,			"""1.12.0""" )			,			"""test requires torch version >= 1.12.0""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_=None			,			lowercase_=None )		->						List[str]:


  '''simple docstring'''






  if test_case is None:
    return partial(lowercase_			,			version=lowercase_ )
  return unittest.skipUnless(is_torch_version(""">="""			,			lowercase_ )			,			F'test requires torch version >= {version}' )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  return unittest.skipUnless(is_tensorboard_available()			,			"""test requires Tensorboard""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  return unittest.skipUnless(is_wandb_available()			,			"""test requires wandb""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  return unittest.skipUnless(is_comet_ml_available()			,			"""test requires comet_ml""" )(lowercase_ )


lowerCamelCase__     :		Any        = (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  return unittest.skipUnless(
      _atleast_one_tracker_available			,			"""test requires at least one tracker to be available and for `comet_ml` to not be installed"""			,			)(lowercase_ )



class    _snake_case (						unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      True


 @classmethod
 def   lowercase__  (       cls):


   '''simple docstring'''




   lowercase__	: Tuple     		=   tempfile.mkdtemp()


 @classmethod
 def   lowercase__  (       cls):


   '''simple docstring'''




   if os.path.exists(cls.tmpdir):
     shutil.rmtree(cls.tmpdir)



 def   lowercase__  (       self):


   '''simple docstring'''




   if self.clear_on_setup:
     for path in Path(self.tmpdir).glob("""**/*"""):
       if path.is_file():
         path.unlink()
       elif path.is_dir():
         shutil.rmtree(SCREAMING_SNAKE_CASE_)



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   super().tearDown()
   # Reset the state of the AcceleratorState singleton.
   AcceleratorState._reset_state()
   PartialState._reset_state()



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   mocks if isinstance(SCREAMING_SNAKE_CASE_       ,	(tuple, list)) else [mocks]
   for m in self.mocks:
     m.start()
     self.addCleanup(m.stop)

def 		UpperCamelCase						(   lowercase_ )		->						Dict:


  '''simple docstring'''






  lowercase__	: Tuple     		=   AcceleratorState()
  lowercase__	: Union[str, Any]     		=   tensor[None].clone().to(state.device )
  lowercase__	: List[str]     		=   gather(lowercase_ ).cpu()
  lowercase__	: List[Any]     		=   tensor[0].cpu()
  for i in range(tensors.shape[0] ):
    if not torch.equal(tensors[i]			,			lowercase_ ):
      return False
  return True



class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   returncode
   lowercase__	: Dict     		=   stdout
   lowercase__	: str     		=   stderr

async def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						List[str]:


  '''simple docstring'''






  while True:
    lowercase__	: str     		=   await stream.readline()
    if line:
      callback(lowercase_ )
    else:
      break

async def 		UpperCamelCase						(   lowercase_			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=False			,			lowercase_=False )		->						_RunOutput:


  '''simple docstring'''






  if echo:
    print("""\nRunning: """			,			""" """.join(lowercase_ ) )

  lowercase__	: int     		=   await asyncio.create_subprocess_exec(
      cmd[0]			,			*cmd[1:]			,			stdin=lowercase_			,			stdout=asyncio.subprocess.PIPE			,			stderr=asyncio.subprocess.PIPE			,			env=lowercase_			,			)

  # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
  # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
  #
  # If it starts hanging, will need to switch to the following code. The problem is that no data
  # will be seen until it's done and if it hangs for example there will be no debug info.
  # out, err = await p.communicate()
  # return _RunOutput(p.returncode, out, err)

  lowercase__	: Optional[Any]     		=   []
  lowercase__	: Optional[Any]     		=   []

  def tee(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_="" ):
    lowercase__	: Union[str, Any]     		=   line.decode("""utf-8""" ).rstrip()
    sink.append(lowercase_ )
    if not quiet:
      print(lowercase_			,			lowercase_			,			file=lowercase_ )

    # XXX: the timeout doesn't seem to make any difference here
  await asyncio.wait(
      [
          asyncio.create_task(_read_stream(p.stdout			,			lambda lowercase_ : tee(lowercase_			,			lowercase_			,			sys.stdout			,			label="""stdout:""" ) ) ),
          asyncio.create_task(_read_stream(p.stderr			,			lambda lowercase_ : tee(lowercase_			,			lowercase_			,			sys.stderr			,			label="""stderr:""" ) ) ),
      ]			,			timeout=lowercase_			,			)
  return _RunOutput(await p.wait()			,			lowercase_			,			lowercase_ )

def 		UpperCamelCase						(   lowercase_			,			lowercase_=None			,			lowercase_=None			,			lowercase_=1_80			,			lowercase_=False			,			lowercase_=True )		->						_RunOutput:


  '''simple docstring'''






  lowercase__	: Dict     		=   asyncio.get_event_loop()
  lowercase__	: List[str]     		=   loop.run_until_complete(
      _stream_subprocess(lowercase_			,			env=lowercase_			,			stdin=lowercase_			,			timeout=lowercase_			,			quiet=lowercase_			,			echo=lowercase_ ) )

  lowercase__	: Dict     		=   """ """.join(lowercase_ )
  if result.returncode > 0:
    lowercase__	: Optional[Any]     		=   """\n""".join(result.stderr )
    raise RuntimeError(
        F'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
        F'The combined stderr from workers follows:\n{stderr}' )

  return result



class    _snake_case (						UpperCAmelCase_				):
 pass

def 		UpperCamelCase						(   lowercase_			,			lowercase_=False )		->						Union[str, Any]:


  '''simple docstring'''






  try:
    lowercase__	: Optional[Any]     		=   subprocess.check_output(lowercase_			,			stderr=subprocess.STDOUT )
    if return_stdout:
      if hasattr(lowercase_			,			"""decode""" ):
        lowercase__	: Tuple     		=   output.decode("""utf-8""" )
      return output
  except subprocess.CalledProcessError as e:
    raise SubprocessCallException(
        F'Command `{" ".join(lowercase_ )}` failed with the following error:\n\n{e.output.decode()}' ) from e








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       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=14       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=99       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=37       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=5_12       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size)

   lowercase__	: Any     		=   None
   if self.use_input_mask:
     lowercase__	: Dict     		=   random_attention_mask([self.batch_size, self.seq_length])

   lowercase__	: 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=SCREAMING_SNAKE_CASE_       ,	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):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.prepare_config_and_inputs()
   lowercase__		,     lowercase__		,     lowercase__	: Optional[Any]     		=   config_and_inputs
   lowercase__	: Optional[Any]     		=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
   return config, inputs_dict


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   20
   lowercase__	: int     		=   model_class_name(SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[Any]     		=   model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   jnp.ones((input_ids.shape[0], max_decoder_length)       ,	dtype="""i4""")

   lowercase__	: Tuple     		=   jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1))
   lowercase__	: List[str]     		=   model(
       input_ids[:, :-1]       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	past_key_values=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)

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

   lowercase__	: Tuple     		=   model(SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   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       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   20
   lowercase__	: List[Any]     		=   model_class_name(SCREAMING_SNAKE_CASE_)

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

   lowercase__	: Dict     		=   model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1))

   lowercase__	: Any     		=   model(
       input_ids[:, :-1]       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	past_key_values=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: int     		=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="""i4""")
   lowercase__	: Tuple     		=   model(
       input_ids[:, -1:]       ,	past_key_values=outputs_cache.past_key_values       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_)

   lowercase__	: 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				):
 __lowerCAmelCase	:  Dict	   =      (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 __lowerCAmelCase	:  str	   =      (FlaxGPTJForCausalLM,) if is_flax_available() else ()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   FlaxGPTJModelTester(self)


 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__		,     lowercase__		,     lowercase__	: Tuple     		=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward_with_attn_mask(
         SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @tooslow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   GPTaTokenizer.from_pretrained("""gpt2"""       ,	pad_token="""<|endoftext|>"""       ,	padding_side="""left""")
   lowercase__	: List[str]     		=   tokenizer(["""Hello this is a long string""", """Hey"""]       ,	return_tensors="""np"""       ,	padding=SCREAMING_SNAKE_CASE_       ,	truncation=SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""")
   lowercase__	: Optional[Any]     		=   False
   lowercase__	: List[str]     		=   model.config.eos_token_id

   lowercase__	: List[Any]     		=   jax.jit(model.generate)

   lowercase__	: Tuple     		=   jit_generate(
       inputs["""input_ids"""]       ,	attention_mask=inputs["""attention_mask"""]       ,	pad_token_id=tokenizer.pad_token_id).sequences

   lowercase__	: List[str]     		=   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       ,	skip_special_tokens=SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   [
       """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(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @is_pt_flax_cross_test
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: int     		=   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[Any]     		=   self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: Any     		=   {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__	: str     		=   getattr(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

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

       lowercase__	: List[str]     		=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: List[Any]     		=   fx_state

       with torch.no_grad():
         lowercase__	: Optional[int]     		=   pt_model(**SCREAMING_SNAKE_CASE_).to_tuple()

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

       with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(SCREAMING_SNAKE_CASE_)
         lowercase__	: List[str]     		=   model_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,	from_pt=SCREAMING_SNAKE_CASE_)

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


 @is_pt_flax_cross_test
 def   lowercase__  (       self):


   '''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__	: Tuple     		=   self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: 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(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

       lowercase__	: str     		=   pt_model_class(SCREAMING_SNAKE_CASE_).eval()
       lowercase__	: Union[str, Any]     		=   model_class(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.floataa)

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

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

       with torch.no_grad():
         lowercase__	: Dict     		=   pt_model(**SCREAMING_SNAKE_CASE_).to_tuple()

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

       with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(SCREAMING_SNAKE_CASE_)
         lowercase__	: Tuple     		=   pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,	from_flax=SCREAMING_SNAKE_CASE_)

       with torch.no_grad():
         lowercase__	: Tuple     		=   pt_model_loaded(**SCREAMING_SNAKE_CASE_).to_tuple()

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



 @tooslow
 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__	: Any     		=   model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""")
     lowercase__	: int     		=   model(np.ones((1, 1)))
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_)






