


import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp
   import numpy as np

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
   import torch
@require_flax
class      UpperCAmelCase_ :

   '''simple docstring'''




   def __init__(		self						,    __A						,    __A=14						,    __A=7						,    __A=True						,    __A=True						,    __A=True						,    __A=99						,    __A=32						,    __A=5						,    __A=4						,    __A=37						,    __A="gelu"						,    __A=0.1						,    __A=0.1						,    __A=512						,    __A=0.02						,    __A=None						,    ):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    parent
     lowerCamelCase			: Dict   =    batch_size
     lowerCamelCase			: str   =    seq_length
     lowerCamelCase			: str   =    is_training
     lowerCamelCase			: Dict   =    use_input_mask
     lowerCamelCase			: List[str]   =    use_labels
     lowerCamelCase			: List[str]   =    vocab_size
     lowerCamelCase			: Dict   =    d_model
     lowerCamelCase			: Union[str, Any]   =    num_hidden_layers
     lowerCamelCase			: Any   =    num_attention_heads
     lowerCamelCase			: Union[str, Any]   =    ffn_dim
     lowerCamelCase			: int   =    activation_function
     lowerCamelCase			: Dict   =    activation_dropout
     lowerCamelCase			: Union[str, Any]   =    attention_dropout
     lowerCamelCase			: Union[str, Any]   =    max_position_embeddings
     lowerCamelCase			: List[Any]   =    initializer_range
     lowerCamelCase			: Union[str, Any]   =    None
     lowerCamelCase			: Dict   =    0
     lowerCamelCase			: List[Any]   =    2
     lowerCamelCase			: Optional[Any]   =    1






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    np.clip(ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size			)						,    3						,    self.vocab_size			)

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

     lowerCamelCase			: str   =    XGLMConfig(
         vocab_size=self.vocab_size						,    d_model=self.hidden_size						,    num_layers=self.num_hidden_layers						,    attention_heads=self.num_attention_heads						,    ffn_dim=self.ffn_dim						,    activation_function=self.activation_function						,    activation_dropout=self.activation_dropout						,    attention_dropout=self.attention_dropout						,    max_position_embeddings=self.max_position_embeddings						,    initializer_range=self.initializer_range						,    use_cache=__A						,    bos_token_id=self.bos_token_id						,    eos_token_id=self.eos_token_id						,    pad_token_id=self.pad_token_id						,    )

     return (config, input_ids, input_mask)






   def _snake_case   (		self			):






     """simple docstring"""





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






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			: Any   =    self.prepare_config_and_inputs()

     lowerCamelCase			: Dict   =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
     lowerCamelCase			: Optional[int]   =    ids_tensor([self.batch_size, self.seq_length]						,    vocab_size=2			)

     return (
         config,
         input_ids,
         attention_mask,
         encoder_hidden_states,
         encoder_attention_mask,
     )






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    __A			):






     """simple docstring"""





     lowerCamelCase			: Any   =    20
     lowerCamelCase			: Optional[Any]   =    model_class_name(__A			)

     lowerCamelCase			: List[Any]   =    model.init_cache(input_ids.shape[0]						,    __A			)
     lowerCamelCase			: Union[str, Any]   =    jnp.ones((input_ids.shape[0], max_decoder_length)						,    dtype="i4"			)

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

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

     lowerCamelCase			: int   =    model(__A			)

     lowerCamelCase			: List[str]   =    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 _snake_case   (		self						,    __A						,    __A						,    __A						,    __A			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    20
     lowerCamelCase			: Optional[Any]   =    model_class_name(__A			)

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

     lowerCamelCase			: int   =    model.init_cache(input_ids.shape[0]						,    __A			)
     lowerCamelCase			: int   =    jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1			)[None, :]						,    (input_ids.shape[0], input_ids.shape[-1] - 1)			)

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

     lowerCamelCase			: Union[str, Any]   =    model(__A						,    attention_mask=__A			)
     lowerCamelCase			: 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_sentencepiece
@require_flax
class      UpperCAmelCase_ ( UpperCamelCase		,			UpperCamelCase		,			unittest.TestCase   ):

   '''simple docstring'''




   __A			:				Tuple							=		(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
   __A			:				str							=		(FlaxXGLMForCausalLM,) if is_flax_available() else ()






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    FlaxXGLMModelTester(self			)






   def _snake_case   (		self			):






     """simple docstring"""





     for model_class_name in self.all_model_classes:
       lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			: List[Any]   =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(__A						,    __A						,    __A						,    __A			)






   def _snake_case   (		self			):






     """simple docstring"""





     for model_class_name in self.all_model_classes:
       lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			: Optional[int]   =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           __A						,    __A						,    __A						,    __A			)






   @slow
   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    XGLMTokenizer.from_pretrained("XGLM"						,    padding_side="left"			)
     lowerCamelCase			: Any   =    tokenizer(["Hello this is a long string", "Hey"]						,    return_tensors="np"						,    padding=__A						,    truncation=__A			)

     lowerCamelCase			: Union[str, Any]   =    FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"			)
     lowerCamelCase			: Tuple   =    1
     lowerCamelCase			: int   =    False

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

     lowerCamelCase			: str   =    jit_generate(inputs["input_ids"]						,    attention_mask=inputs["attention_mask"]			).sequences

     lowerCamelCase			: int   =    tokenizer.batch_decode(__A						,    skip_special_tokens=__A			)

     lowerCamelCase			: Optional[Any]   =    [
         "Hello this is a long string of questions, but I'm not sure if I'm",
         "Hey, I'm a newbie to the forum and I'",
     ]

     self.assertListEqual(__A						,    __A			)






   @is_pt_flax_cross_test
   def _snake_case   (		self			):






     """simple docstring"""





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

         # load corresponding PyTorch class
         lowerCamelCase			: int   =    model_class.__name__[4:]  # Skip the "Flax" at the beginning
         lowerCamelCase			: Union[str, Any]   =    getattr(__A						,    __A			)

         lowerCamelCase   ,     lowerCamelCase			: Union[str, Any]   =    pt_inputs["input_ids"].shape
         lowerCamelCase			: Optional[Any]   =    np.random.randint(0						,    seq_length - 1						,    size=(batch_size,)			)
         for batch_idx, start_index in enumerate(__A			):
           lowerCamelCase			: Tuple   =    0
           lowerCamelCase			: List[Any]   =    1
           lowerCamelCase			: Dict   =    0
           lowerCamelCase			: Union[str, Any]   =    1
         lowerCamelCase			: int   =    pt_model_class(__A			).eval()
         # Flax models don't use the `use_cache` option and cache is not returned as a default.
         # So we disable `use_cache` here for PyTorch model.
         lowerCamelCase			: Any   =    False
         lowerCamelCase			: Optional[Any]   =    model_class(__A						,    dtype=jnp.floataa			)
         lowerCamelCase			: Union[str, Any]   =    convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,    __A			)
         lowerCamelCase			: List[Any]   =    fx_state

         with torch.no_grad():
           lowerCamelCase			: Any   =    pt_model(**__A			).to_tuple()

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

         with tempfile.TemporaryDirectory() as tmpdirname:
           pt_model.save_pretrained(__A			)
           lowerCamelCase			: Union[str, Any]   =    model_class.from_pretrained(__A						,    from_pt=__A			)

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






   @is_pt_flax_cross_test
   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase   ,     lowerCamelCase			: 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
         lowerCamelCase			: Tuple   =    self._prepare_for_class(__A						,    __A			)
         lowerCamelCase			: Tuple   =    {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

         lowerCamelCase			: List[str]   =    pt_model_class(__A			).eval()
         lowerCamelCase			: Optional[Any]   =    False
         lowerCamelCase			: Optional[Any]   =    model_class(__A						,    dtype=jnp.floataa			)

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

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

         with torch.no_grad():
           lowerCamelCase			: Union[str, Any]   =    pt_model(**__A			).to_tuple()

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

         with tempfile.TemporaryDirectory() as tmpdirname:
           fx_model.save_pretrained(__A			)
           lowerCamelCase			: Union[str, Any]   =    pt_model_class.from_pretrained(__A						,    from_flax=__A			)

         with torch.no_grad():
           lowerCamelCase			: List[Any]   =    pt_model_loaded(**__A			).to_tuple()

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






   @slow
   def _snake_case   (		self			):






     """simple docstring"""





     for model_class_name in self.all_model_classes:
       lowerCamelCase			: str   =    model_class_name.from_pretrained("facebook/xglm-564M"			)
       lowerCamelCase			: Optional[Any]   =    model(np.ones((1, 1)			)			)
       self.assertIsNotNone(__A			)







def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if number != int(SCREAMING_SNAKE_CASE_     ):
    raise ValueError("factorial() only accepts integral values"     )
  if number < 0:
    raise ValueError("factorial() not defined for negative values"     )
  lowerCamelCase			: Optional[Any]   =    1
  for i in range(1				,						number + 1     ):
    value *= i
  return value






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if not isinstance(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):
    raise ValueError("factorial() only accepts integral values"     )
  if n < 0:
    raise ValueError("factorial() not defined for negative values"     )
  return 1 if n == 0 or n == 1 else n * factorial(n - 1     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _snake_case					=					int(input('''Enter a positive integer: ''').strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')



