






"""simple docstring"""






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	inspect.getfile(accelerate.test_utils)
          SCREAMING_SNAKE_CASE_   :						str							=	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_script.py'''])
          SCREAMING_SNAKE_CASE_   :						Tuple							=	os.path.sep.join(
              mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''])
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_ops.py'''])
      @require_multi_gpu
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          print(F'Found {torch.cuda.device_count()} devices.')
          SCREAMING_SNAKE_CASE_   :						Dict							=	['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
          with patch_environment(omp_num_threads=1):
              execute_subprocess_async(UpperCamelCase__		,			env=os.environ.copy())
      @require_multi_gpu
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          print(F'Found {torch.cuda.device_count()} devices.')
          SCREAMING_SNAKE_CASE_   :						List[str]							=	['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
          print(F'Command: {cmd}')
          with patch_environment(omp_num_threads=1):
              execute_subprocess_async(UpperCamelCase__		,			env=os.environ.copy())
      @require_multi_gpu
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
          with patch_environment(omp_num_threads=1):
              execute_subprocess_async(UpperCamelCase__		,			env=os.environ.copy())



      @require_multi_gpu
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          print(F'Found {torch.cuda.device_count()} devices, using 2 devices only')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
          with patch_environment(omp_num_threads=1		,			cuda_visible_devices='''0,1'''):
              execute_subprocess_async(UpperCamelCase__		,			env=os.environ.copy())


if __name__ == "__main__":
      UpperCAmelCase_     :				List[Any]         =				Accelerator()
      UpperCAmelCase_     :				List[str]         =				(accelerator.state.process_index + 2, 10)
      UpperCAmelCase_     :				Union[str, Any]         =				torch.randint(0, 10, shape).to(accelerator.device)

      UpperCAmelCase_     :				str         =				""""""

      UpperCAmelCase_     :				List[Any]         =				accelerator.pad_across_processes(tensor)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
            error_msg += "Padding was not done with the right value (0)."

      UpperCAmelCase_     :				Any         =				accelerator.pad_across_processes(tensor, pad_first=True)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      UpperCAmelCase_     :				List[Any]         =				accelerator.state.num_processes - accelerator.state.process_index - 1
      if not torch.equal(tensora[index:], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[:index] == 0):
            error_msg += "Padding was not done with the right value (0)."

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)










"""simple docstring"""






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 							lowerCAmelCase__       :






      '''simple docstring'''

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




          '''simple docstring'''


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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size)

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

          SCREAMING_SNAKE_CASE_   :						Union[str, 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=lowercase_		,			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 _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	config_and_inputs
          SCREAMING_SNAKE_CASE_   :						int							=	{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Any		,			lowercase_  :						Any		,			lowercase_  :						Tuple):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model.init_cache(input_ids.shape[0]		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.ones((input_ids.shape[0], max_decoder_length)		,			dtype='''i4''')

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

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

          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	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 _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Tuple		,			lowercase_  :						Optional[int]		,			lowercase_  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	20
          SCREAMING_SNAKE_CASE_   :						List[str]							=	model_class_name(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[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]		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1)[None, :]		,			(input_ids.shape[0], input_ids.shape[-1] - 1))

          SCREAMING_SNAKE_CASE_   :						Dict							=	model(
              input_ids[:, :-1]		,			attention_mask=lowercase_		,			past_key_values=lowercase_		,			position_ids=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,			dtype='''i4''')
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model(
              input_ids[:, -1:]		,			past_key_values=outputs_cache.past_key_values		,			attention_mask=lowercase_		,			position_ids=lowercase_		,			)

          SCREAMING_SNAKE_CASE_   :						Any							=	model(lowercase_		,			attention_mask=lowercase_)

          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 							lowerCAmelCase__       (    UpperCAmelCase__       ,	UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      __UpperCamelCase							=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	FlaxGPTJModelTester(self)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						int							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(lowercase_		,			lowercase_		,			lowercase_		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						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(
                  lowercase_		,			lowercase_		,			lowercase_		,			lowercase_)
      @tooslow
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_   :						List[str]							=	FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	False
          SCREAMING_SNAKE_CASE_   :						str							=	model.config.eos_token_id

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jax.jit(model.generate)

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

          SCREAMING_SNAKE_CASE_   :						int							=	tokenizer.batch_decode(lowercase_		,			skip_special_tokens=lowercase_)

          SCREAMING_SNAKE_CASE_   :						int							=	[
              '''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 _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[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
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self._prepare_for_class(lowercase_		,			lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Any							=	{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

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

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

                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	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_)
                      SCREAMING_SNAKE_CASE_   :						int							=	model_class.from_pretrained(lowercase_		,			from_pt=lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	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 _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''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_   :						Tuple							=	self._prepare_for_class(lowercase_		,			lowercase_)
                  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_   :						Optional[int]							=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  SCREAMING_SNAKE_CASE_   :						Dict							=	getattr(lowercase_		,			lowercase_)

                  SCREAMING_SNAKE_CASE_   :						int							=	pt_model_class(lowercase_).eval()
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model_class(lowercase_		,			dtype=jnp.floataa)

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

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

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

                  SCREAMING_SNAKE_CASE_   :						str							=	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_)
                      SCREAMING_SNAKE_CASE_   :						List[str]							=	pt_model_class.from_pretrained(lowercase_		,			from_flax=lowercase_)

                  with torch.no_grad():
                      SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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)



      @tooslow
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_   :						Any							=	model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
              SCREAMING_SNAKE_CASE_   :						List[str]							=	model(np.ones((1, 1)))
              self.assertIsNotNone(lowercase_)


