





'''simple docstring'''






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  lowerCamelCase__				:


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=1_4   ,     UpperCAmelCase__=7   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=False   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=9_9   ,     UpperCAmelCase__=3_2   ,     UpperCAmelCase__=4   ,     UpperCAmelCase__=4   ,     UpperCAmelCase__=[[["global", "local"], 2]]   ,     UpperCAmelCase__=3_7   ,     UpperCAmelCase__="gelu"   ,     UpperCAmelCase__=0.1   ,     UpperCAmelCase__=0.1   ,     UpperCAmelCase__=5_1_2   ,     UpperCAmelCase__=7   ,     UpperCAmelCase__=0.0_2   ,     )       ->				Dict:
					_A :				Tuple				=  parent
					_A :				Union[str, Any]				=  batch_size
					_A :				Optional[int]				=  seq_length
					_A :				Any				=  is_training
					_A :				Optional[Any]				=  use_input_mask
					_A :				List[Any]				=  use_token_type_ids
					_A :				List[Any]				=  use_labels
					_A :				Optional[int]				=  vocab_size
					_A :				Tuple				=  hidden_size
					_A :				Optional[int]				=  num_hidden_layers
					_A :				Union[str, Any]				=  num_attention_heads
					_A :				str				=  attention_types
					_A :				Optional[int]				=  intermediate_size
					_A :				int				=  hidden_act
					_A :				str				=  hidden_dropout_prob
					_A :				str				=  attention_probs_dropout_prob
					_A :				Dict				=  max_position_embeddings
					_A :				Tuple				=  window_size
					_A :				List[str]				=  initializer_range
					_A :				Union[str, Any]				=  None
					_A :				Any				=  vocab_size - 1
					_A :				str				=  vocab_size - 1
					_A :				int				=  vocab_size - 1




	def 				_lowerCamelCase				(					self			)       ->				int:
					_A :				Optional[int]				=  ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size			)

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

					_A :				Any				=  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=UpperCAmelCase__   ,     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 				_lowerCamelCase				(					self			)       ->				Optional[Any]:
					_A :				str				=  self.prepare_config_and_inputs()
					_A	,       _A	,       _A :				Optional[Any]				=  config_and_inputs
					_A :				str				=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Tuple:
					_A :				Union[str, Any]				=  2_0
					_A :				Optional[Any]				=  model_class_name(UpperCAmelCase__			)

					_A :				Tuple				=  model.init_cache(input_ids.shape[0]   ,     UpperCAmelCase__			)
					_A :				int				=  jnp.ones((input_ids.shape[0], max_decoder_length)   ,     dtype='''i4'''			)

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

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

					_A :				Optional[int]				=  model(UpperCAmelCase__			)

					_A :				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 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Dict:
					_A :				int				=  2_0
					_A :				Any				=  model_class_name(UpperCAmelCase__			)

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

					_A :				List[Any]				=  model.init_cache(input_ids.shape[0]   ,     UpperCAmelCase__			)
					_A :				Optional[Any]				=  jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1			)[None, :]   ,     (input_ids.shape[0], input_ids.shape[-1] - 1)			)

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

					_A :				Tuple				=  model(UpperCAmelCase__   ,     attention_mask=UpperCAmelCase__			)

					_A :				Tuple				=  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__				(						snake_case_  ,							snake_case_  ,							unittest.TestCase						):


	"""simple docstring"""





	__magic_name__							   =		(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
	__magic_name__							   =		(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




	def 				_lowerCamelCase				(					self			)       ->				List[str]:
					_A :				Any				=  FlaxGPTNeoModelTester(self			)




	def 				_lowerCamelCase				(					self			)       ->				Optional[int]:
					for model_class_name in self.all_model_classes:
									_A	,       _A	,       _A :				Dict				=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self			)       ->				Union[str, Any]:
					for model_class_name in self.all_model_classes:
									_A	,       _A	,       _A :				int				=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)




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

					_A :				str				=  FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''			)
					_A :				Dict				=  False
					_A :				Optional[Any]				=  model.config.eos_token_id

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

					_A :				Optional[int]				=  jit_generate(
					    inputs['''input_ids''']   ,     attention_mask=inputs['''attention_mask''']   ,     pad_token_id=tokenizer.pad_token_id			).sequences

					_A :				Tuple				=  tokenizer.batch_decode(UpperCAmelCase__   ,     skip_special_tokens=UpperCAmelCase__			)

					_A :				Union[str, 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(UpperCAmelCase__   ,     UpperCAmelCase__			)




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

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

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

													_A :				Dict				=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,     UpperCAmelCase__			)
													_A :				Optional[int]				=  fx_state

													with torch.no_grad():
																	_A :				Tuple				=  pt_model(**UpperCAmelCase__			).to_tuple()

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																	pt_model.save_pretrained(UpperCAmelCase__			)
																	_A :				Tuple				=  model_class.from_pretrained(UpperCAmelCase__   ,     from_pt=UpperCAmelCase__			)

													_A :				Union[str, Any]				=  fx_model_loaded(**UpperCAmelCase__			).to_tuple()
													self.assertEqual(
													    len(UpperCAmelCase__			)   ,     len(UpperCAmelCase__			)   ,     '''Output lengths differ between Flax and PyTorch'''			)
													for fx_output_loaded, pt_output in zip(UpperCAmelCase__   ,     UpperCAmelCase__			):
																	self.assert_almost_equals(fx_output_loaded[:, -1]   ,     pt_output[:, -1].numpy()   ,     4e-2			)




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

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

													_A :				List[str]				=  pt_model_class(UpperCAmelCase__			).eval()
													_A :				int				=  model_class(UpperCAmelCase__   ,     dtype=jnp.floataa			)

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

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

													with torch.no_grad():
																	_A :				Dict				=  pt_model(**UpperCAmelCase__			).to_tuple()

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																	fx_model.save_pretrained(UpperCAmelCase__			)
																	_A :				Optional[Any]				=  pt_model_class.from_pretrained(UpperCAmelCase__   ,     from_flax=UpperCAmelCase__			)

													with torch.no_grad():
																	_A :				Any				=  pt_model_loaded(**UpperCAmelCase__			).to_tuple()

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




	@slow
	def 				_lowerCamelCase				(					self			)       ->				str:
					for model_class_name in self.all_model_classes:
									_A :				Tuple				=  model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''			)
									_A :				Union[str, Any]				=  model(np.ones((1, 1)			)			)
									self.assertIsNotNone(UpperCAmelCase__			)







'''simple docstring'''






import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


__UpperCamelCase      :   List[Any]								=     get_logger(__name__)


if TYPE_CHECKING:
	from .builder import DatasetBuilder





def      lowercase  (  lowerCAmelCase :						List[str]   , lowerCAmelCase :						Optional[Union[str, bool]] = None):



				"""simple docstring"""


				_A :				Union[str, Any]				=  importlib.import_module(lowerCAmelCase)

				# TODO(QL): always update the module to add subsequent new authentication
				if hasattr(lowerCAmelCase   , '''_patched_for_streaming''') and module._patched_for_streaming:
								return

				def wrap_auth(lowerCAmelCase :						Union[str, Any]):
								@wraps(lowerCAmelCase)
								def wrapper(*lowerCAmelCase :						Union[str, Any]   , **lowerCAmelCase :						int):
												return function(*lowerCAmelCase   , use_auth_token=lowerCAmelCase   , **lowerCAmelCase)

								_A :				Optional[Any]				=  '''wrap_auth'''
								return wrapper

				# open files in a streaming fashion
				patch_submodule(lowerCAmelCase   , '''open'''   , wrap_auth(lowerCAmelCase)).start()
				patch_submodule(lowerCAmelCase   , '''os.listdir'''   , wrap_auth(lowerCAmelCase)).start()
				patch_submodule(lowerCAmelCase   , '''os.walk'''   , wrap_auth(lowerCAmelCase)).start()
				patch_submodule(lowerCAmelCase   , '''glob.glob'''   , wrap_auth(lowerCAmelCase)).start()
				# allow to navigate in remote zip files
				patch_submodule(lowerCAmelCase   , '''os.path.join'''   , lowerCAmelCase).start()
				patch_submodule(lowerCAmelCase   , '''os.path.dirname'''   , lowerCAmelCase).start()
				patch_submodule(lowerCAmelCase   , '''os.path.basename'''   , lowerCAmelCase).start()
				patch_submodule(lowerCAmelCase   , '''os.path.relpath'''   , lowerCAmelCase).start()
				patch_submodule(lowerCAmelCase   , '''os.path.split'''   , lowerCAmelCase).start()
				patch_submodule(lowerCAmelCase   , '''os.path.splitext'''   , lowerCAmelCase).start()
				# allow checks on paths
				patch_submodule(lowerCAmelCase   , '''os.path.exists'''   , wrap_auth(lowerCAmelCase)).start()
				patch_submodule(lowerCAmelCase   , '''os.path.isdir'''   , wrap_auth(lowerCAmelCase)).start()
				patch_submodule(lowerCAmelCase   , '''os.path.isfile'''   , wrap_auth(lowerCAmelCase)).start()
				patch_submodule(lowerCAmelCase   , '''os.path.getsize'''   , wrap_auth(lowerCAmelCase)).start()
				patch_submodule(lowerCAmelCase   , '''pathlib.Path'''   , lowerCAmelCase).start()
				# file readers
				patch_submodule(lowerCAmelCase   , '''gzip.open'''   , wrap_auth(lowerCAmelCase)).start()
				patch_submodule(lowerCAmelCase   , '''numpy.load'''   , wrap_auth(lowerCAmelCase)).start()
				patch_submodule(lowerCAmelCase   , '''pandas.read_csv'''   , wrap_auth(lowerCAmelCase)   , attrs=['''__version__''']).start()
				patch_submodule(lowerCAmelCase   , '''pandas.read_excel'''   , wrap_auth(lowerCAmelCase)   , attrs=['''__version__''']).start()
				patch_submodule(lowerCAmelCase   , '''scipy.io.loadmat'''   , wrap_auth(lowerCAmelCase)   , attrs=['''__version__''']).start()
				patch_submodule(lowerCAmelCase   , '''xml.etree.ElementTree.parse'''   , wrap_auth(lowerCAmelCase)).start()
				patch_submodule(lowerCAmelCase   , '''xml.dom.minidom.parse'''   , wrap_auth(lowerCAmelCase)).start()
				_A :				Optional[int]				=  True





def      lowercase  (  lowerCAmelCase :						"DatasetBuilder"):



				"""simple docstring"""


				extend_module_for_streaming(builder.__module__   , use_auth_token=builder.use_auth_token)
				# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
				if not builder.__module__.startswith('''datasets.'''):  # check that it's not a packaged builder like csv
								for imports in get_imports(inspect.getfile(builder.__class__)):
												if imports[0] == "internal":
																_A :				Optional[int]				=  imports[1]
																_A :				str				=  '''.'''.join(builder.__module__.split('''.''')[:-1] + [internal_import_name])
																extend_module_for_streaming(lowerCAmelCase   , use_auth_token=builder.use_auth_token)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
				from .builder import DatasetBuilder

				_A :				Tuple				=  [
				    cls.__module__
				    for cls in type(lowerCAmelCase).__mro__[1:]  # make sure it's not the same module we've already patched
				    if issubclass(cls   , lowerCAmelCase) and cls.__module__ != DatasetBuilder.__module__
				]  # check it's not a standard builder from datasets.builder
				for module in parent_builder_modules:
								extend_module_for_streaming(lowerCAmelCase   , use_auth_token=builder.use_auth_token)
