




"""simple docstring"""







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					,				lowerCamelCase_					,				lowerCamelCase_=1_4					,				lowerCamelCase_=7					,				lowerCamelCase_=True					,				lowerCamelCase_=True					,				lowerCamelCase_=True					,				lowerCamelCase_=9_9					,				lowerCamelCase_=3_2					,				lowerCamelCase_=5					,				lowerCamelCase_=4					,				lowerCamelCase_=3_7					,				lowerCamelCase_="gelu"					,				lowerCamelCase_=0.1					,				lowerCamelCase_=0.1					,				lowerCamelCase_=5_1_2					,				lowerCamelCase_=0.02					,				lowerCamelCase_=None					,				)							->      List[Any]:
       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)							->      Optional[int]:
       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=lowerCamelCase_					,				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)							->      Dict:
       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      UpperCAmelCase__ (		self)							->      Tuple:
       UpperCamelCase					,  UpperCamelCase					,  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					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      str:
       UpperCamelCase										=  2_0
       UpperCamelCase										=  model_class_name(lowerCamelCase_)

       UpperCamelCase										=  model.init_cache(input_ids.shape[0]					,				lowerCamelCase_)
       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=lowerCamelCase_					,				past_key_values=lowerCamelCase_					,				position_ids=lowerCamelCase_					,				)

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

       UpperCamelCase										=  model(lowerCamelCase_)

       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					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      List[str]:
       UpperCamelCase										=  2_0
       UpperCamelCase										=  model_class_name(lowerCamelCase_)

       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]					,				lowerCamelCase_)
       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=lowerCamelCase_					,				past_key_values=lowerCamelCase_					,				position_ids=lowerCamelCase_					,				)
       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=lowerCamelCase_					,				position_ids=lowerCamelCase_					,				)

       UpperCamelCase										=  model(lowerCamelCase_					,				attention_mask=lowerCamelCase_)
       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_ (				lowerCamelCase_  ,   lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
   A_										=					(FlaxXGLMForCausalLM,) if is_flax_available() else ()







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  FlaxXGLMModelTester(self)







   def      UpperCAmelCase__ (		self)							->      Tuple:
       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(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       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(
               lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







   @slow
   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  XGLMTokenizer.from_pretrained('''XGLM'''					,				padding_side='''left''')
       UpperCamelCase										=  tokenizer(['''Hello this is a long string''', '''Hey''']					,				return_tensors='''np'''					,				padding=lowerCamelCase_					,				truncation=lowerCamelCase_)

       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(lowerCamelCase_					,				skip_special_tokens=lowerCamelCase_)

       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(lowerCamelCase_					,				lowerCamelCase_)







   @is_pt_flax_cross_test
   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       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(lowerCamelCase_					,				lowerCamelCase_)
               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(lowerCamelCase_					,				lowerCamelCase_)

               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(lowerCamelCase_):
                   UpperCamelCase										=  0
                   UpperCamelCase										=  1
                   UpperCamelCase										=  0
                   UpperCamelCase										=  1
               UpperCamelCase										=  pt_model_class(lowerCamelCase_).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(lowerCamelCase_					,				dtype=jnp.floataa)
               UpperCamelCase										=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,				lowerCamelCase_)
               UpperCamelCase										=  fx_state

               with torch.no_grad():
                   UpperCamelCase										=  pt_model(**lowerCamelCase_).to_tuple()

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                   pt_model.save_pretrained(lowerCamelCase_)
                   UpperCamelCase										=  model_class.from_pretrained(lowerCamelCase_					,				from_pt=lowerCamelCase_)

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







   @is_pt_flax_cross_test
   def      UpperCAmelCase__ (		self)							->      List[str]:
       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(lowerCamelCase_					,				lowerCamelCase_)
               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(lowerCamelCase_					,				lowerCamelCase_)

               UpperCamelCase										=  pt_model_class(lowerCamelCase_).eval()
               UpperCamelCase										=  False
               UpperCamelCase										=  model_class(lowerCamelCase_					,				dtype=jnp.floataa)

               UpperCamelCase										=  load_flax_weights_in_pytorch_model(lowerCamelCase_					,				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(lowerCamelCase_):
                   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(**lowerCamelCase_).to_tuple()

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                   fx_model.save_pretrained(lowerCamelCase_)
                   UpperCamelCase										=  pt_model_class.from_pretrained(lowerCamelCase_					,				from_flax=lowerCamelCase_)

               with torch.no_grad():
                   UpperCamelCase										=  pt_model_loaded(**lowerCamelCase_).to_tuple()

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







   @slow
   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       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(lowerCamelCase_)





"""simple docstring"""







import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					CustomConfig







   def __init__(		self					,				lowerCamelCase_)							->      List[Any]:
       super().__init__(lowerCamelCase_)
       UpperCamelCase										=  torch.nn.Linear(config.hidden_size					,				config.hidden_size)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[Any]:
       return self.linear(lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      List[str]:
       pass

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					NoSuperInitConfig







   def __init__(		self					,				lowerCamelCase_)							->      Optional[int]:
       super().__init__(lowerCamelCase_)
       UpperCamelCase										=  torch.nn.Linear(config.attribute					,				config.attribute)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      int:
       return self.linear(lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Any:
       pass