code: 
  
  
  import tempfile
  import unittest
  
  import numpy as np
  
  import transformers
  from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel
  
  if is_torch_available():
   import torch
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case=1_4    ,      __snake_case=7    ,      __snake_case=True    ,      __snake_case=True    ,      __snake_case=False    ,      __snake_case=True    ,      __snake_case=9_9    ,      __snake_case=3_2    ,      __snake_case=5    ,      __snake_case=4    ,      __snake_case=3_7    ,      __snake_case="gelu"    ,      __snake_case=0.1    ,      __snake_case=0.1    ,      __snake_case=5_1_2    ,      __snake_case=0.02    ,      ):
           snake_case					=       parent
           snake_case					=       batch_size
           snake_case					=       seq_length
           snake_case					=       is_training
           snake_case					=       use_input_mask
           snake_case					=       use_token_type_ids
           snake_case					=       use_labels
           snake_case					=       vocab_size
           snake_case					=       hidden_size
           snake_case					=       num_hidden_layers
           snake_case					=       num_attention_heads
           snake_case					=       intermediate_size
           snake_case					=       hidden_act
           snake_case					=       hidden_dropout_prob
           snake_case					=       attention_probs_dropout_prob
           snake_case					=       max_position_embeddings
           snake_case					=       initializer_range
           snake_case					=       None
           snake_case					=       vocab_size - 1
           snake_case					=       vocab_size - 1
           snake_case					=       vocab_size - 1
      def 	a_    ( self					):
           snake_case					=       ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size					)
  
           snake_case					=       None
           if self.use_input_mask:
                snake_case					=       random_attention_mask([self.batch_size, self.seq_length]					)
  
           snake_case					=       GPTaConfig(
               vocab_size=self.vocab_size    ,      n_embd=self.hidden_size    ,      n_layer=self.num_hidden_layers    ,      n_head=self.num_attention_heads    ,      n_positions=self.max_position_embeddings    ,      use_cache=__snake_case    ,      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 	a_    ( self					):
           snake_case					=       self.prepare_config_and_inputs()
           snake_case				,						snake_case				,						snake_case					=       config_and_inputs
           snake_case					=       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict
      def 	a_    ( self					):
           snake_case				,						snake_case				,						snake_case					=       self.prepare_config_and_inputs()
  
           snake_case					=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
           snake_case					=       ids_tensor([self.batch_size, self.seq_length]    ,      vocab_size=2					)
  
           return (
               config,
               input_ids,
               attention_mask,
               encoder_hidden_states,
               encoder_attention_mask,
           )
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           snake_case					=       2_0
           snake_case					=       model_class_name(__snake_case					)
  
           snake_case					=       model.init_cache(input_ids.shape[0]    ,      __snake_case					)
           snake_case					=       jnp.ones((input_ids.shape[0], max_decoder_length)    ,      dtype='''i4'''					)
  
           snake_case					=       jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1					)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)					)
           snake_case					=       model(
               input_ids[:, :-1]    ,      attention_mask=__snake_case    ,      past_key_values=__snake_case    ,      position_ids=__snake_case    ,      )
  
           snake_case					=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype='''i4'''					)
           snake_case					=       model(
               input_ids[:, -1:]    ,      attention_mask=__snake_case    ,      past_key_values=outputs_cache.past_key_values    ,      position_ids=__snake_case    ,      )
  
           snake_case					=       model(__snake_case					)
  
           snake_case					=       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 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           snake_case					=       2_0
           snake_case					=       model_class_name(__snake_case					)
  
           snake_case					=       jnp.concatenate(
               [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)]    ,      axis=-1    ,      )
  
           snake_case					=       model.init_cache(input_ids.shape[0]    ,      __snake_case					)
           snake_case					=       jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1					)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)					)
  
           snake_case					=       model(
               input_ids[:, :-1]    ,      attention_mask=__snake_case    ,      past_key_values=__snake_case    ,      position_ids=__snake_case    ,      )
           snake_case					=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype='''i4'''					)
           snake_case					=       model(
               input_ids[:, -1:]    ,      past_key_values=outputs_cache.past_key_values    ,      attention_mask=__snake_case    ,      position_ids=__snake_case    ,      )
  
           snake_case					=       model(__snake_case    ,      attention_mask=__snake_case					)
  
           snake_case					=       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 						A__      (					snake_case__ ,      snake_case__ ,      unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
      __magic_name__    						=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()
      def 	a_    ( self					):
           snake_case					=       FlaxGPTaModelTester(self					)
      def 	a_    ( self					):
           for model_class_name in self.all_model_classes:
                snake_case				,						snake_case				,						snake_case					=       self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward(__snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					)
      def 	a_    ( self					):
           for model_class_name in self.all_model_classes:
                snake_case				,						snake_case				,						snake_case					=       self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward_with_attn_mask(
                    __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					)
      @slow
      def 	a_    ( self					):
           snake_case					=       GPTaTokenizer.from_pretrained('''gpt2'''    ,      pad_token='''</s>'''    ,      padding_side='''left'''					)
           snake_case					=       tokenizer(['''Hello this is a long string''', '''Hey''']    ,      return_tensors='''np'''    ,      padding=__snake_case    ,      truncation=__snake_case					)
  
           snake_case					=       FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''					)
           snake_case					=       False
           snake_case					=       model.config.eos_token_id
  
           snake_case					=       jax.jit(model.generate					)
  
           snake_case					=       jit_generate(inputs['''input_ids''']    ,      attention_mask=inputs['''attention_mask''']					).sequences
  
           snake_case					=       tokenizer.batch_decode(__snake_case    ,      skip_special_tokens=__snake_case					)
  
           snake_case					=       [
               '''Hello this is a long string of words. I\'m going to start with the first one.\n''',
               '''Hey, I\'m not sure if I\'m going to be able to do''',
           ]
  
           self.assertListEqual(__snake_case    ,      __snake_case					)
      @is_pt_flax_cross_test
      def 	a_    ( self					):
           snake_case				,						snake_case					=       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
                     snake_case					=       self._prepare_for_class(__snake_case    ,      __snake_case					)
                     snake_case					=       {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}
  
                     # load corresponding PyTorch class
                     snake_case					=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
                     snake_case					=       getattr(__snake_case    ,      __snake_case					)
  
                     snake_case				,						snake_case					=       pt_inputs['''input_ids'''].shape
                     snake_case					=       np.random.randint(0    ,      seq_length - 1    ,      size=(batch_size,)					)
                     for batch_idx, start_index in enumerate(__snake_case					):
                          snake_case					=       0
                          snake_case					=       1
                          snake_case					=       0
                          snake_case					=       1
                     snake_case					=       pt_model_class(__snake_case					).eval()
                     snake_case					=       model_class(__snake_case    ,      dtype=jnp.floataa					)
  
                     snake_case					=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,      __snake_case					)
                     snake_case					=       fx_state
  
                     with torch.no_grad():
                          snake_case					=       pt_model(**__snake_case					).to_tuple()
  
                     snake_case					=       fx_model(**__snake_case					).to_tuple()
                     self.assertEqual(len(__snake_case					)    ,      len(__snake_case					)    ,      '''Output lengths differ between Flax and PyTorch'''					)
                     for fx_output, pt_output in zip(__snake_case    ,      __snake_case					):
                          self.assert_almost_equals(fx_output[:, -1]    ,      pt_output[:, -1].numpy()    ,      4E-2					)
  
                     with tempfile.TemporaryDirectory() as tmpdirname:
                          pt_model.save_pretrained(__snake_case					)
                          snake_case					=       model_class.from_pretrained(__snake_case    ,      from_pt=__snake_case					)
  
                     snake_case					=       fx_model_loaded(**__snake_case					).to_tuple()
                     self.assertEqual(
                         len(__snake_case					)    ,      len(__snake_case					)    ,      '''Output lengths differ between Flax and PyTorch'''					)
                     for fx_output_loaded, pt_output in zip(__snake_case    ,      __snake_case					):
                          self.assert_almost_equals(fx_output_loaded[:, -1]    ,      pt_output[:, -1].numpy()    ,      4E-2					)
      @is_pt_flax_cross_test
      def 	a_    ( self					):
           snake_case				,						snake_case					=       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
                     snake_case					=       self._prepare_for_class(__snake_case    ,      __snake_case					)
                     snake_case					=       {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}
  
                     # load corresponding PyTorch class
                     snake_case					=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
                     snake_case					=       getattr(__snake_case    ,      __snake_case					)
  
                     snake_case					=       pt_model_class(__snake_case					).eval()
                     snake_case					=       model_class(__snake_case    ,      dtype=jnp.floataa					)
  
                     snake_case					=       load_flax_weights_in_pytorch_model(__snake_case    ,      fx_model.params					)
                     snake_case				,						snake_case					=       pt_inputs['''input_ids'''].shape
                     snake_case					=       np.random.randint(0    ,      seq_length - 1    ,      size=(batch_size,)					)
                     for batch_idx, start_index in enumerate(__snake_case					):
                          snake_case					=       0
                          snake_case					=       1
                          snake_case					=       0
                          snake_case					=       1
  
                     # make sure weights are tied in PyTorch
                     pt_model.tie_weights()
  
                     with torch.no_grad():
                          snake_case					=       pt_model(**__snake_case					).to_tuple()
  
                     snake_case					=       fx_model(**__snake_case					).to_tuple()
                     self.assertEqual(len(__snake_case					)    ,      len(__snake_case					)    ,      '''Output lengths differ between Flax and PyTorch'''					)
                     for fx_output, pt_output in zip(__snake_case    ,      __snake_case					):
                          self.assert_almost_equals(fx_output[:, -1]    ,      pt_output[:, -1].numpy()    ,      4E-2					)
  
                     with tempfile.TemporaryDirectory() as tmpdirname:
                          fx_model.save_pretrained(__snake_case					)
                          snake_case					=       pt_model_class.from_pretrained(__snake_case    ,      from_flax=__snake_case					)
  
                     with torch.no_grad():
                          snake_case					=       pt_model_loaded(**__snake_case					).to_tuple()
  
                     self.assertEqual(
                         len(__snake_case					)    ,      len(__snake_case					)    ,      '''Output lengths differ between Flax and PyTorch'''					)
                     for fx_output, pt_output in zip(__snake_case    ,      __snake_case					):
                          self.assert_almost_equals(fx_output[:, -1]    ,      pt_output[:, -1].numpy()    ,      4E-2					)
  
  
  
  
      @slow
      def 	a_    ( self					):
           for model_class_name in self.all_model_classes:
                snake_case					=       model_class_name.from_pretrained('''gpt2'''    ,      from_pt=__snake_case					)
                snake_case					=       model(np.ones((1, 1)					)					)
                self.assertIsNotNone(__snake_case					)
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  