code: 
  
  
  """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    __A       :
  
  
  
  
  
  
   """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				,					)      ->  List[str]:
       a					=parent
       a					=batch_size
       a					=seq_length
       a					=is_training
       a					=use_input_mask
       a					=use_labels
       a					=vocab_size
       a					=d_model
       a					=num_hidden_layers
       a					=num_attention_heads
       a					=ffn_dim
       a					=activation_function
       a					=activation_dropout
       a					=attention_dropout
       a					=max_position_embeddings
       a					=initializer_range
       a					=None
       a					=0
       a					=2
       a					=1
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       a					=np.clip(ids_tensor([self.batch_size, self.seq_length]				,					self.vocab_size		)				,					3				,					self.vocab_size		)
  
       a					=None
       if self.use_input_mask:
           a					=random_attention_mask([self.batch_size, self.seq_length]		)
  
       a					=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    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       a					=self.prepare_config_and_inputs()
       a	,   a	,   a					=config_and_inputs
       a					={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       a	,   a	,   a					=self.prepare_config_and_inputs()
  
       a					=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
       a					=ids_tensor([self.batch_size, self.seq_length]				,					vocab_size=2		)
  
       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A		)      ->  int:
       a					=20
       a					=model_class_name(__A		)
  
       a					=model.init_cache(input_ids.shape[0]				,					__A		)
       a					=jnp.ones((input_ids.shape[0], max_decoder_length)				,					dtype='''i4'''		)
  
       a					=jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1		)[None, :]				,					(input_ids.shape[0], input_ids.shape[-1] - 1)		)
       a					=model(
           input_ids[:, :-1]				,					attention_mask=__A				,					past_key_values=__A				,					position_ids=__A				,					)
  
       a					=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,					dtype='''i4'''		)
       a					=model(
           input_ids[:, -1:]				,					attention_mask=__A				,					past_key_values=outputs_cache.past_key_values				,					position_ids=__A				,					)
  
       a					=model(__A		)
  
       a					=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    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A		)      ->  Optional[int]:
       a					=20
       a					=model_class_name(__A		)
  
       a					=jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])		)]				,					axis=-1				,					)
  
       a					=model.init_cache(input_ids.shape[0]				,					__A		)
       a					=jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1		)[None, :]				,					(input_ids.shape[0], input_ids.shape[-1] - 1)		)
  
       a					=model(
           input_ids[:, :-1]				,					attention_mask=__A				,					past_key_values=__A				,					position_ids=__A				,					)
       a					=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,					dtype='''i4'''		)
       a					=model(
           input_ids[:, -1:]				,					past_key_values=outputs_cache.past_key_values				,					attention_mask=__A				,					position_ids=__A				,					)
  
       a					=model(__A				,					attention_mask=__A		)
       a					=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    __A       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
   __lowerCAmelCase				      =						(FlaxXGLMForCausalLM,) if is_flax_available() else ()
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       a					=FlaxXGLMModelTester(self		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
       for model_class_name in self.all_model_classes:
           a	,   a	,   a					=self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(__A				,					__A				,					__A				,					__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       for model_class_name in self.all_model_classes:
           a	,   a	,   a					=self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               __A				,					__A				,					__A				,					__A		)
  
  
   @slow
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       a					=XGLMTokenizer.from_pretrained('''XGLM'''				,					padding_side='''left'''		)
       a					=tokenizer(['''Hello this is a long string''', '''Hey''']				,					return_tensors='''np'''				,					padding=__A				,					truncation=__A		)
  
       a					=FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''		)
       a					=1
       a					=False
  
       a					=jax.jit(model.generate		)
  
       a					=jit_generate(inputs['''input_ids''']				,					attention_mask=inputs['''attention_mask''']		).sequences
  
       a					=tokenizer.batch_decode(__A				,					skip_special_tokens=__A		)
  
       a					=[
           '''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    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       a	,   a					=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					=self._prepare_for_class(__A				,					__A		)
               a					={k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}
  
               # load corresponding PyTorch class
               a					=model_class.__name__[4:]  # Skip the "Flax" at the beginning
               a					=getattr(__A				,					__A		)
  
               a	,   a					=pt_inputs['''input_ids'''].shape
               a					=np.random.randint(0				,					seq_length - 1				,					size=(batch_size,)		)
               for batch_idx, start_index in enumerate(__A		):
                   a					=0
                   a					=1
                   a					=0
                   a					=1
               a					=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.
               a					=False
               a					=model_class(__A				,					dtype=jnp.floataa		)
               a					=convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,					__A		)
               a					=fx_state
  
               with torch.no_grad():
                   a					=pt_model(**__A		).to_tuple()
  
               a					=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		)
                   a					=model_class.from_pretrained(__A				,					from_pt=__A		)
  
               a					=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    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       a	,   a					=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					=self._prepare_for_class(__A				,					__A		)
               a					={k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}
  
               # load corresponding PyTorch class
               a					=model_class.__name__[4:]  # Skip the "Flax" at the beginning
               a					=getattr(__A				,					__A		)
  
               a					=pt_model_class(__A		).eval()
               a					=False
               a					=model_class(__A				,					dtype=jnp.floataa		)
  
               a					=load_flax_weights_in_pytorch_model(__A				,					fx_model.params		)
               a	,   a					=pt_inputs['''input_ids'''].shape
               a					=np.random.randint(0				,					seq_length - 1				,					size=(batch_size,)		)
               for batch_idx, start_index in enumerate(__A		):
                   a					=0
                   a					=1
                   a					=0
                   a					=1
  
               # make sure weights are tied in PyTorch
               pt_model.tie_weights()
  
               with torch.no_grad():
                   a					=pt_model(**__A		).to_tuple()
  
               a					=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		)
                   a					=pt_model_class.from_pretrained(__A				,					from_flax=__A		)
  
               with torch.no_grad():
                   a					=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    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       for model_class_name in self.all_model_classes:
           a					=model_class_name.from_pretrained('''facebook/xglm-564M'''		)
           a					=model(np.ones((1, 1)		)		)
           self.assertIsNotNone(__A		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  lowerCamelCase_     :     Any    		=						{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}
  
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase_     :     List[str]    		=						[
             """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
             """DinatForImageClassification""",
             """DinatModel""",
             """DinatPreTrainedModel""",
             """DinatBackbone""",
         ]
  
  if TYPE_CHECKING:
         from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_dinat import (
                    DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DinatBackbone,
                    DinatForImageClassification,
                    DinatModel,
                    DinatPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase_     :     List[Any]    		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)