
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 		lowercase_       :

 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=0.02     ,	):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


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

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

        UpperCamelCase_							       =       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     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       2_0
        UpperCamelCase_							       =       model_class_name(__UpperCamelCase     )

        UpperCamelCase_							       =       model.init_cache(input_ids.shape[0]     ,	__UpperCamelCase     )
        UpperCamelCase_							       =       jnp.ones((input_ids.shape[0], max_decoder_length)     ,	dtype="""i4"""     )

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

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

        UpperCamelCase_							       =       model(__UpperCamelCase     )

        UpperCamelCase_							       =       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     ):




        """simple docstring"""


        UpperCamelCase_							       =       2_0
        UpperCamelCase_							       =       model_class_name(__UpperCamelCase     )

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

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

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

        UpperCamelCase_							       =       model(__UpperCamelCase     ,	attention_mask=__UpperCamelCase     )

        UpperCamelCase_							       =       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 		lowercase_       (       __SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	unittest.TestCase				):
 A__						:				str         =    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 A__						:				Dict         =    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       FlaxGPTNeoModelTester(self     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        for model_class_name in self.all_model_classes:
               UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        for model_class_name in self.all_model_classes:
               UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       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     ):




        """simple docstring"""


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

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

        UpperCamelCase_							       =       jax.jit(model.generate     )

        UpperCamelCase_							       =       jit_generate(
            inputs["""input_ids"""]     ,	attention_mask=inputs["""attention_mask"""]     ,	pad_token_id=tokenizer.pad_token_id     ).sequences

        UpperCamelCase_							       =       tokenizer.batch_decode(__UpperCamelCase     ,	skip_special_tokens=__UpperCamelCase     )

        UpperCamelCase_							       =       [
            """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     ):




        """simple docstring"""


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

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

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

                      UpperCamelCase_							       =       convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,	__UpperCamelCase     )
                      UpperCamelCase_							       =       fx_state

                      with torch.no_grad():
                             UpperCamelCase_							       =       pt_model(**__UpperCamelCase     ).to_tuple()

                      UpperCamelCase_							       =       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     )
                             UpperCamelCase_							       =       model_class.from_pretrained(__UpperCamelCase     ,	from_pt=__UpperCamelCase     )

                      UpperCamelCase_							       =       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     ):




        """simple docstring"""


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

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

                      UpperCamelCase_							       =       pt_model_class(__UpperCamelCase     ).eval()
                      UpperCamelCase_							       =       model_class(__UpperCamelCase     ,	dtype=jnp.floataa     )

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

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

                      with torch.no_grad():
                             UpperCamelCase_							       =       pt_model(**__UpperCamelCase     ).to_tuple()

                      UpperCamelCase_							       =       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     )
                             UpperCamelCase_							       =       pt_model_class.from_pretrained(__UpperCamelCase     ,	from_flax=__UpperCamelCase     )

                      with torch.no_grad():
                             UpperCamelCase_							       =       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     ):




        """simple docstring"""


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





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


_A	  =					get_logger(__name__)


if TYPE_CHECKING:
      from .builder import DatasetBuilder




def 				lowerCamelCase__				(						a__  :  int	,      a__  :  Optional[Union[str, bool]] = None     )		->	Dict:
       UpperCamelCase_							       =       importlib.import_module(a__     )

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

       def wrap_auth(a__  :  int     ):
              @wraps(a__     )
              def wrapper(*a__  :  int	,      **a__  :  List[str]     ):
                     return function(*a__	,      use_auth_token=a__	,      **a__     )

              UpperCamelCase_							       =       """wrap_auth"""
              return wrapper

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







def 				lowerCamelCase__				(						a__  :  "DatasetBuilder"     )		->	Tuple:
       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":
                            UpperCamelCase_							       =       imports[1]
                            UpperCamelCase_							       =       """.""".join(builder.__module__.split("""."""     )[:-1] + [internal_import_name]     )
                            extend_module_for_streaming(a__	,      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

       UpperCamelCase_							       =       [
           cls.__module__
           for cls in type(a__     ).__mro__[1:]  # make sure it's not the same module we've already patched
           if issubclass(cls	,      a__     ) 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(a__	,      use_auth_token=builder.use_auth_token     )



