




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     snake_case  :


   '''simple docstring'''







   def __init__(					self     :	List[Any]						,    lowerCamelCase_     :	Any						,    lowerCamelCase_     :	Optional[int]=14						,    lowerCamelCase_     :	Union[str, Any]=7						,    lowerCamelCase_     :	List[Any]=True						,    lowerCamelCase_     :	List[str]=True						,    lowerCamelCase_     :	List[str]=True						,    lowerCamelCase_     :	Dict=99						,    lowerCamelCase_     :	List[Any]=32						,    lowerCamelCase_     :	int=5						,    lowerCamelCase_     :	int=4						,    lowerCamelCase_     :	Optional[int]=37						,    lowerCamelCase_     :	List[str]="gelu"						,    lowerCamelCase_     :	Optional[Any]=0.1						,    lowerCamelCase_     :	List[Any]=0.1						,    lowerCamelCase_     :	str=512						,    lowerCamelCase_     :	List[Any]=0.02						,    lowerCamelCase_     :	Dict=None						,    )					->Optional[int]:
     '''simple docstring'''







     UpperCAmelCase__    						=   parent
     UpperCAmelCase__    						=   batch_size
     UpperCAmelCase__    						=   seq_length
     UpperCAmelCase__    						=   is_training
     UpperCAmelCase__    						=   use_input_mask
     UpperCAmelCase__    						=   use_labels
     UpperCAmelCase__    						=   vocab_size
     UpperCAmelCase__    						=   d_model
     UpperCAmelCase__    						=   num_hidden_layers
     UpperCAmelCase__    						=   num_attention_heads
     UpperCAmelCase__    						=   ffn_dim
     UpperCAmelCase__    						=   activation_function
     UpperCAmelCase__    						=   activation_dropout
     UpperCAmelCase__    						=   attention_dropout
     UpperCAmelCase__    						=   max_position_embeddings
     UpperCAmelCase__    						=   initializer_range
     UpperCAmelCase__    						=   None
     UpperCAmelCase__    						=   0
     UpperCAmelCase__    						=   2
     UpperCAmelCase__    						=   1




   def 	UpperCAmelCase       (					self     :	List[Any]			)					->str:
     '''simple docstring'''







     UpperCAmelCase__    						=   np.clip(ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size			)						,    3						,    self.vocab_size			)

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

     UpperCAmelCase__    						=   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 	UpperCAmelCase       (					self     :	Any			)					->Dict:
     '''simple docstring'''







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




   def 	UpperCAmelCase       (					self     :	str			)					->List[Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.prepare_config_and_inputs()

     UpperCAmelCase__    						=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
     UpperCAmelCase__    						=   ids_tensor([self.batch_size, self.seq_length]						,    vocab_size=2			)

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




   def 	UpperCAmelCase       (					self     :	str						,    lowerCamelCase_     :	Tuple						,    lowerCamelCase_     :	Tuple						,    lowerCamelCase_     :	Union[str, Any]						,    lowerCamelCase_     :	Tuple			)					->Optional[Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   20
     UpperCAmelCase__    						=   model_class_name(_A			)

     UpperCAmelCase__    						=   model.init_cache(input_ids.shape[0]						,    _A			)
     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=_A						,    past_key_values=_A						,    position_ids=_A						,    )

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

     UpperCAmelCase__    						=   model(_A			)

     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 	UpperCAmelCase       (					self     :	int						,    lowerCamelCase_     :	Any						,    lowerCamelCase_     :	Tuple						,    lowerCamelCase_     :	Optional[Any]						,    lowerCamelCase_     :	str			)					->Optional[int]:
     '''simple docstring'''







     UpperCAmelCase__    						=   20
     UpperCAmelCase__    						=   model_class_name(_A			)

     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]						,    _A			)
     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=_A						,    past_key_values=_A						,    position_ids=_A						,    )
     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=_A						,    position_ids=_A						,    )

     UpperCAmelCase__    						=   model(_A						,    attention_mask=_A			)
     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_sentencepiece
@require_flax
class     snake_case  (	lowercase__ ,  lowercase__ ,  unittest.TestCase							):


   '''simple docstring'''







   UpperCamelCase__	:       List[Any]     =      (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
   UpperCamelCase__	:       Tuple     =      (FlaxXGLMForCausalLM,) if is_flax_available() else ()




   def 	UpperCAmelCase       (					self     :	int			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   FlaxXGLMModelTester(self			)




   def 	UpperCAmelCase       (					self     :	int			)					->Any:
     '''simple docstring'''







     for model_class_name in self.all_model_classes:
       UpperCAmelCase__    						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(_A						,    _A						,    _A						,    _A			)




   def 	UpperCAmelCase       (					self     :	Any			)					->Any:
     '''simple docstring'''







     for model_class_name in self.all_model_classes:
       UpperCAmelCase__    						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           _A						,    _A						,    _A						,    _A			)




   @slow
   def 	UpperCAmelCase       (					self     :	Union[str, Any]			)					->Optional[Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   XGLMTokenizer.from_pretrained("""XGLM"""						,    padding_side="""left"""			)
     UpperCAmelCase__    						=   tokenizer(["""Hello this is a long string""", """Hey"""]						,    return_tensors="""np"""						,    padding=_A						,    truncation=_A			)

     UpperCAmelCase__    						=   FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)
     UpperCAmelCase__    						=   1
     UpperCAmelCase__    						=   False

     UpperCAmelCase__    						=   jax.jit(model.generate			)

     UpperCAmelCase__    						=   jit_generate(inputs["""input_ids"""]						,    attention_mask=inputs["""attention_mask"""]			).sequences

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

     UpperCAmelCase__    						=   [
         '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 	UpperCAmelCase       (					self     :	Any			)					->Optional[int]:
     '''simple docstring'''







     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(_A						,    _A			)
         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(_A						,    _A			)

         UpperCAmelCase__    						=   pt_inputs['input_ids'].shape
         UpperCAmelCase__    						=   np.random.randint(0						,    seq_length - 1						,    size=(batch_size,)			)
         for batch_idx, start_index in enumerate(_A			):
           UpperCAmelCase__    						=   0
           UpperCAmelCase__    						=   1
           UpperCAmelCase__    						=   0
           UpperCAmelCase__    						=   1
         UpperCAmelCase__    						=   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.
         UpperCAmelCase__    						=   False
         UpperCAmelCase__    						=   model_class(_A						,    dtype=jnp.floataa			)
         UpperCAmelCase__    						=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,    _A			)
         UpperCAmelCase__    						=   fx_state

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

         UpperCAmelCase__    						=   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			)
           UpperCAmelCase__    						=   model_class.from_pretrained(_A						,    from_pt=_A			)

         UpperCAmelCase__    						=   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 	UpperCAmelCase       (					self     :	List[Any]			)					->Any:
     '''simple docstring'''







     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(_A						,    _A			)
         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(_A						,    _A			)

         UpperCAmelCase__    						=   pt_model_class(_A			).eval()
         UpperCAmelCase__    						=   False
         UpperCAmelCase__    						=   model_class(_A						,    dtype=jnp.floataa			)

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

         UpperCAmelCase__    						=   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			)
           UpperCAmelCase__    						=   pt_model_class.from_pretrained(_A						,    from_flax=_A			)

         with torch.no_grad():
           UpperCAmelCase__    						=   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 	UpperCAmelCase       (					self     :	Optional[int]			)					->int:
     '''simple docstring'''







     for model_class_name in self.all_model_classes:
       UpperCAmelCase__    						=   model_class_name.from_pretrained("""facebook/xglm-564M"""			)
       UpperCAmelCase__    						=   model(np.ones((1, 1)			)			)
       self.assertIsNotNone(_A			)










from typing import TYPE_CHECKING

from ...utils import _LazyModule


snake_case__ :						List[str]     =			{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
   from .tokenization_phobert import PhobertTokenizer

else:
   import sys

   snake_case__ :						Tuple     =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




