code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import copy
  import os
  import tempfile
  from importlib import import_module
  from math import isnan
  
  from transformers import is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow
  
  from ..test_modeling_tf_common import ids_tensor
  
  
  if is_tf_available():
   import numpy as np
   import tensorflow as tf
  
   from transformers import (
       TF_MODEL_FOR_CAUSAL_LM_MAPPING,
       TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_MASKED_LM_MAPPING,
       TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
       TF_MODEL_FOR_PRETRAINING_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       TFSharedEmbeddings,
   )
  
   if _tf_gpu_memory_limit is not None:
    __snake_case							      =						tf.config.list_physical_devices("""GPU""")
    for gpu in gpus:
     # Restrict TensorFlow to only allocate x GB of memory on the GPUs
     try:
      tf.config.set_logical_device_configuration(
          gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
      )
      __snake_case							      =						tf.config.list_logical_devices("""GPU""")
      print("""Logical GPUs""", logical_gpus)
     except RuntimeError as e:
      # Virtual devices must be set before GPUs have been initialized
      print(e)
  
  
  
  
  
  
  
  @require_tf
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      List[str]     =None
        UpperCamelCase_       :      str     =()
        UpperCamelCase_       :      Optional[Any]     =()
        UpperCamelCase_       :      int     =True
        UpperCamelCase_       :      Tuple     =True
        UpperCamelCase_       :      int     =True
        UpperCamelCase_       :      int     =False
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=False				)       ->   dict:
            UpperCamelCase :Any										=     copy.deepcopy(SCREAMING_SNAKE_CASE_				)
  
            if model_class in get_values(SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :List[str]										=     {
                    k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_					,       1				)					,       (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)				)
                    if isinstance(SCREAMING_SNAKE_CASE_					,       tf.Tensor				) and v.ndim > 0
                    else v
                    for k, v in inputs_dict.items()
                }
  
            if return_labels:
                if model_class in get_values(SCREAMING_SNAKE_CASE_				):
                    UpperCamelCase :Any										=     tf.ones(self.model_tester.batch_size					,       dtype=tf.intaa				)
                elif model_class in get_values(SCREAMING_SNAKE_CASE_				):
                    UpperCamelCase :Union[str, Any]										=     tf.zeros(self.model_tester.batch_size					,       dtype=tf.intaa				)
                    UpperCamelCase :int										=     tf.zeros(self.model_tester.batch_size					,       dtype=tf.intaa				)
                elif model_class in [
                    *get_values(SCREAMING_SNAKE_CASE_				),
                    *get_values(SCREAMING_SNAKE_CASE_				),
                ]:
                    UpperCamelCase :Any										=     tf.zeros(self.model_tester.batch_size					,       dtype=tf.intaa				)
                elif model_class in get_values(SCREAMING_SNAKE_CASE_				):
                    UpperCamelCase :int										=     tf.zeros(self.model_tester.batch_size					,       dtype=tf.intaa				)
                elif model_class in [
                    *get_values(SCREAMING_SNAKE_CASE_				),
                    *get_values(SCREAMING_SNAKE_CASE_				),
                    *get_values(SCREAMING_SNAKE_CASE_				),
                    *get_values(SCREAMING_SNAKE_CASE_				),
                    *get_values(SCREAMING_SNAKE_CASE_				),
                ]:
                    UpperCamelCase :Any										=     tf.zeros(
                        (self.model_tester.batch_size, self.model_tester.seq_length)					,       dtype=tf.intaa				)
            return inputs_dict
        @slow
        def 					UpperCAmelCase		(       self				)       ->   int:
            UpperCamelCase							,     UpperCamelCase :List[Any]										=     self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes[:2]:
                UpperCamelCase :Tuple										=     self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :Dict										=     model_class(SCREAMING_SNAKE_CASE_				)
  
                @tf.function
                def run_in_graph_mode():
                    return model(SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :List[Any]										=     run_in_graph_mode()
                self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)
        @slow
        def 					UpperCAmelCase		(       self				)       ->   int:
            UpperCamelCase							,     UpperCamelCase :Tuple										=     self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes[:2]:
                UpperCamelCase :Union[str, Any]										=     self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :int										=     model_class(SCREAMING_SNAKE_CASE_				)
  
                @tf.function(experimental_compile=SCREAMING_SNAKE_CASE_				)
                def run_in_graph_mode():
                    return model(SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :Union[str, Any]										=     run_in_graph_mode()
                self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)
        @slow
        def 					UpperCAmelCase		(       self				)       ->   Dict:
            # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
            UpperCamelCase							,     UpperCamelCase :Optional[int]										=     self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes[:2]:
                UpperCamelCase :Union[str, Any]										=     model_class(SCREAMING_SNAKE_CASE_				)
                if getattr(SCREAMING_SNAKE_CASE_					,       '''hf_compute_loss'''					,       SCREAMING_SNAKE_CASE_				):
                    # Test that model correctly compute the loss with kwargs
                    UpperCamelCase :str										=     self._prepare_for_class(inputs_dict.copy()					,       SCREAMING_SNAKE_CASE_					,       return_labels=SCREAMING_SNAKE_CASE_				)
                    # Is there a better way to remove these decoder inputs?
                    UpperCamelCase :Union[str, Any]										=     {
                        key: val
                        for key, val in prepared_for_class.items()
                        if key not in ('''head_mask''', '''decoder_head_mask''', '''cross_attn_head_mask''', '''decoder_input_ids''')
                    }
  
                    UpperCamelCase :str										=     {
                        '''labels''',
                        '''label''',
                        '''label_ids''',
                        '''start_positions''',
                        '''start_position''',
                        '''end_positions''',
                        '''end_position''',
                        '''next_sentence_label''',
                    }
                    UpperCamelCase :str										=     possible_label_cols.intersection(set(SCREAMING_SNAKE_CASE_				)				)
                    self.assertGreater(len(SCREAMING_SNAKE_CASE_				)					,       0					,       msg='''No matching label names found!'''				)
                    UpperCamelCase :Tuple										=     {key: val for key, val in prepared_for_class.items() if key in label_names}
                    UpperCamelCase :Any										=     {key: val for key, val in prepared_for_class.items() if key not in label_names}
                    self.assertGreater(len(SCREAMING_SNAKE_CASE_				)					,       0				)
  
                    # Make sure it works with XLA!
                    model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)					,       jit_compile=SCREAMING_SNAKE_CASE_				)
                    # Make sure the model fits without crashing regardless of where we pass the labels
                    UpperCamelCase :Optional[int]										=     model.fit(
                        SCREAMING_SNAKE_CASE_					,       validation_data=SCREAMING_SNAKE_CASE_					,       steps_per_epoch=1					,       validation_steps=1					,       shuffle=SCREAMING_SNAKE_CASE_					,       verbose=0					,       )
                    UpperCamelCase :List[str]										=     history.history['''loss'''][0]
                    self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_				)				)
                    UpperCamelCase :Tuple										=     history.history['''val_loss'''][0]
                    self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_				)				)
  
                    # Now test it with separate labels, to make sure that path works in XLA too.
                    UpperCamelCase :Optional[Any]										=     model_class(SCREAMING_SNAKE_CASE_				)
                    model.compile(optimizer=tf.keras.optimizers.SGD(0.0				)					,       jit_compile=SCREAMING_SNAKE_CASE_				)
                    UpperCamelCase :str										=     model.fit(
                        SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       validation_data=(inputs_minus_labels, labels)					,       steps_per_epoch=1					,       validation_steps=1					,       shuffle=SCREAMING_SNAKE_CASE_					,       verbose=0					,       )
  
                    UpperCamelCase :str										=     history.history['''loss'''][0]
                    self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_				)				)
                    UpperCamelCase :str										=     history.history['''val_loss'''][0]
                    self.assertTrue(not isnan(SCREAMING_SNAKE_CASE_				)				)
        @slow
        def 					UpperCAmelCase		(       self				)       ->   Any:
            UpperCamelCase							,     UpperCamelCase :Optional[Any]										=     self.model_tester.prepare_config_and_inputs_for_common()
            UpperCamelCase :Dict										=     True
            UpperCamelCase :Tuple										=     True
  
            if hasattr(SCREAMING_SNAKE_CASE_					,       '''use_cache'''				):
                UpperCamelCase :int										=     True
  
            UpperCamelCase :str										=     getattr(self.model_tester					,       '''encoder_seq_length'''					,       self.model_tester.seq_length				)
            UpperCamelCase :Any										=     getattr(self.model_tester					,       '''key_length'''					,       SCREAMING_SNAKE_CASE_				)
  
            for model_class in self.all_model_classes[:2]:
                UpperCamelCase :Tuple										=     self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :Union[str, Any]										=     model_class(SCREAMING_SNAKE_CASE_				)
                model.build()
                UpperCamelCase :str										=     len(model(SCREAMING_SNAKE_CASE_				)				)
  
                for key in list(class_inputs_dict.keys()				):
                    # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                    if key not in model.input_signature:
                        del class_inputs_dict[key]
                    # Check it's a tensor, in case the inputs dict has some bools in it too
                    elif isinstance(class_inputs_dict[key]					,       tf.Tensor				) and class_inputs_dict[key].dtype.is_integer:
                        UpperCamelCase :int										=     tf.cast(class_inputs_dict[key]					,       tf.intaa				)
  
                if set(class_inputs_dict.keys()				) != set(model.input_signature.keys()				):
                    continue  # Some models have inputs that the preparation functions don't create, we skip those
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(SCREAMING_SNAKE_CASE_					,       saved_model=SCREAMING_SNAKE_CASE_				)
                    UpperCamelCase :int										=     os.path.join(SCREAMING_SNAKE_CASE_					,       '''saved_model'''					,       '''1'''				)
                    UpperCamelCase :Optional[int]										=     tf.keras.models.load_model(SCREAMING_SNAKE_CASE_				)
                    UpperCamelCase :Dict										=     model(SCREAMING_SNAKE_CASE_				)
  
                    if self.is_encoder_decoder:
                        UpperCamelCase :Any										=     outputs['''encoder_hidden_states''']
                        UpperCamelCase :List[str]										=     outputs['''encoder_attentions''']
                    else:
                        UpperCamelCase :Dict										=     outputs['''hidden_states''']
                        UpperCamelCase :str										=     outputs['''attentions''']
  
                    self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)
  
                    UpperCamelCase :List[str]										=     getattr(
                        self.model_tester					,       '''expected_num_hidden_layers'''					,       self.model_tester.num_hidden_layers + 1				)
  
                    self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_				)
                    self.assertListEqual(
                        list(output_hidden_states[0].shape[-2:]				)					,       [self.model_tester.seq_length, self.model_tester.hidden_size]					,       )
  
                    self.assertEqual(len(SCREAMING_SNAKE_CASE_				)					,       self.model_tester.num_hidden_layers				)
                    self.assertListEqual(
                        list(output_attentions[0].shape[-3:]				)					,       [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]					,       )
        @slow
        def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
            tf.keras.mixed_precision.set_global_policy('''mixed_float16'''				)
  
            # try/finally block to ensure subsequent tests run in float32
            try:
                UpperCamelCase							,     UpperCamelCase :Union[str, Any]										=     self.model_tester.prepare_config_and_inputs_for_common()
                for model_class in self.all_model_classes[:2]:
                    UpperCamelCase :Optional[Any]										=     self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
                    UpperCamelCase :List[str]										=     model_class(SCREAMING_SNAKE_CASE_				)
                    UpperCamelCase :Dict										=     model(SCREAMING_SNAKE_CASE_				)
  
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)
            finally:
                tf.keras.mixed_precision.set_global_policy('''float32'''				)
        @slow
        def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
            UpperCamelCase							,     UpperCamelCase :Dict										=     self.model_tester.prepare_config_and_inputs_for_common()
            # head_mask and decoder_head_mask has different shapes than other input args
            if "head_mask" in inputs_dict:
                del inputs_dict["head_mask"]
            if "decoder_head_mask" in inputs_dict:
                del inputs_dict["decoder_head_mask"]
            if "cross_attn_head_mask" in inputs_dict:
                del inputs_dict["cross_attn_head_mask"]
            UpperCamelCase :Dict										=     {
                module_member
                for model_class in self.all_model_classes
                for module in (import_module(model_class.__module__				),)
                for module_member_name in dir(SCREAMING_SNAKE_CASE_				)
                if module_member_name.endswith('''MainLayer'''				)
                for module_member in (getattr(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				),)
                if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
                and tf.keras.layers.Layer in module_member.__bases__
                and getattr(SCREAMING_SNAKE_CASE_					,       '''_keras_serializable'''					,       SCREAMING_SNAKE_CASE_				)
            }
  
            for main_layer_class in tf_main_layer_classes:
                # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
                if "T5" in main_layer_class.__name__:
                    # Take the same values than in TFT5ModelTester for this shared layer
                    UpperCamelCase :List[str]										=     TFSharedEmbeddings(self.model_tester.vocab_size					,       self.model_tester.hidden_size					,       name='''shared'''				)
                    UpperCamelCase :Optional[Any]										=     False
                    UpperCamelCase :Optional[Any]										=     main_layer_class(SCREAMING_SNAKE_CASE_					,       embed_tokens=SCREAMING_SNAKE_CASE_				)
                else:
                    UpperCamelCase :Union[str, Any]										=     main_layer_class(SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :Tuple										=     {
                    name: tf.keras.Input(tensor.shape[1:]					,       dtype=tensor.dtype				) for name, tensor in inputs_dict.items()
                }
  
                if hasattr(self.model_tester					,       '''num_labels'''				):
                    UpperCamelCase :Union[str, Any]										=     self.model_tester.num_labels
                else:
                    UpperCamelCase :List[str]										=     2
  
                UpperCamelCase :List[str]										=     tf.data.Dataset.from_tensor_slices(
                    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)				))				).batch(1				)
  
                UpperCamelCase :int										=     main_layer(SCREAMING_SNAKE_CASE_				)[0]
                UpperCamelCase :Any										=     tf.keras.layers.Dense(SCREAMING_SNAKE_CASE_					,       activation='''softmax'''					,       name='''outputs'''				)(SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :List[Any]										=     tf.keras.models.Model(inputs=SCREAMING_SNAKE_CASE_					,       outputs=[outputs]				)
  
                model.compile(loss='''binary_crossentropy'''					,       optimizer='''adam'''					,       metrics=['''binary_accuracy''']				)
                model.fit(SCREAMING_SNAKE_CASE_					,       epochs=1				)
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                    UpperCamelCase :Any										=     os.path.join(SCREAMING_SNAKE_CASE_					,       '''keras_model.h5'''				)
                    model.save(SCREAMING_SNAKE_CASE_				)
                    if "T5" in main_layer_class.__name__:
                        UpperCamelCase :List[str]										=     tf.keras.models.load_model(
                            SCREAMING_SNAKE_CASE_					,       custom_objects={
                                main_layer_class.__name__: main_layer_class,
                                '''TFSharedEmbeddings''': TFSharedEmbeddings,
                            }					,       )
                    else:
                        UpperCamelCase :Dict										=     tf.keras.models.load_model(
                            SCREAMING_SNAKE_CASE_					,       custom_objects={main_layer_class.__name__: main_layer_class}				)
                    assert isinstance(SCREAMING_SNAKE_CASE_					,       tf.keras.Model				)
                    model(SCREAMING_SNAKE_CASE_				)
        @slow
        def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
            UpperCamelCase							,     UpperCamelCase :Dict										=     self.model_tester.prepare_config_and_inputs_for_common()
  
            for model_class in self.all_model_classes[:2]:
                UpperCamelCase :str										=     model_class(SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :int										=     copy.deepcopy(SCREAMING_SNAKE_CASE_				)
  
                if not self.is_encoder_decoder:
                    UpperCamelCase :int										=     inputs['''input_ids''']
                    del inputs["input_ids"]
                else:
                    UpperCamelCase :Any										=     inputs['''input_ids''']
                    UpperCamelCase :Dict										=     inputs.get('''decoder_input_ids'''					,       SCREAMING_SNAKE_CASE_				)
                    del inputs["input_ids"]
                    inputs.pop('''decoder_input_ids'''					,       SCREAMING_SNAKE_CASE_				)
  
                if not self.is_encoder_decoder:
                    UpperCamelCase :List[str]										=     model.get_input_embeddings()(SCREAMING_SNAKE_CASE_				)
                else:
                    UpperCamelCase :int										=     model.get_input_embeddings()(SCREAMING_SNAKE_CASE_				)
                    UpperCamelCase :str										=     model.get_input_embeddings()(SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :str										=     self._prepare_for_class(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
                @tf.function
                def run_in_graph_mode():
                    return model(SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :Tuple										=     run_in_graph_mode()
                self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Optional[int]:
            # special tokens cannot be bad tokens
            UpperCamelCase :Tuple										=     []
            if model.config.bos_token_id is not None:
                special_tokens.append(model.config.bos_token_id				)
            if model.config.pad_token_id is not None:
                special_tokens.append(model.config.pad_token_id				)
            if model.config.eos_token_id is not None:
                special_tokens.append(model.config.eos_token_id				)
  
            # create random bad tokens that are not special tokens
            UpperCamelCase :Optional[Any]										=     []
            while len(SCREAMING_SNAKE_CASE_				) < num_bad_tokens:
                UpperCamelCase :Tuple										=     tf.squeeze(ids_tensor((1, 1)					,       self.model_tester.vocab_size				)					,       0				).numpy()[0]
                if token not in special_tokens:
                    bad_tokens.append(SCREAMING_SNAKE_CASE_				)
            return bad_tokens
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   List[str]:
            for token_id in output_ids[0].numpy().tolist():
                self.assertGreaterEqual(SCREAMING_SNAKE_CASE_					,       0				)
                self.assertLess(SCREAMING_SNAKE_CASE_					,       self.model_tester.vocab_size				)
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Dict:
            # for all bad word tokens
            for bad_word_ids in bad_words_ids:
                # for all slices in batch
                for generated_ids_slice in generated_ids:
                    # for all word idx
                    for i in range(len(SCREAMING_SNAKE_CASE_				)					,       len(SCREAMING_SNAKE_CASE_				)				):
                        # if tokens match
                        if generated_ids_slice[i - len(SCREAMING_SNAKE_CASE_				) : i] == bad_word_ids:
                            return True
            return False
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __snake_case							      =						{
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizer"""]
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						[
       """OpenLlamaForCausalLM""",
       """OpenLlamaModel""",
       """OpenLlamaPreTrainedModel""",
       """OpenLlamaForSequenceClassification""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_open_llama import (
        OpenLlamaForCausalLM,
        OpenLlamaForSequenceClassification,
        OpenLlamaModel,
        OpenLlamaPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __snake_case							      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  