





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.xglm.modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
      )





@require_tf
class UpperCAmelCase_				:
  __lowerCAmelCase   :						List[str]							=   XGLMConfig
  __lowerCAmelCase   :						Union[str, Any]							=   {}
  __lowerCAmelCase   :						str							=   """gelu"""


  def __init__(							self ,__snake_case ,__snake_case=1_4 ,__snake_case=7 ,__snake_case=True ,__snake_case=True ,__snake_case=True ,__snake_case=9_9 ,__snake_case=3_2 ,__snake_case=2 ,__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 ,):



       """simple docstring"""





       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 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return XGLMConfig.from_pretrained('''facebook/xglm-564M''' )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       tf.clip_by_value(
           ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size ) ,clip_value_min=0 ,clip_value_max=3 )

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

       A_          =       self.get_config()

       A_          =       floats_tensor([self.num_hidden_layers, self.num_attention_heads] ,2 )

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
       )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return 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=snake_case__ ,bos_token_id=self.bos_token_id ,eos_token_id=self.eos_token_id ,pad_token_id=self.pad_token_id ,return_dict=snake_case__ ,)




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.prepare_config_and_inputs()

       (
           A_  
       )          =       config_and_inputs

       A_          =       {
           'input_ids': input_ids,
           'head_mask': head_mask,
       }

       return config, inputs_dict





@require_tf
class UpperCAmelCase_				( SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		unittest.TestCase ):
  __lowerCAmelCase   :						Any							=   (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
  __lowerCAmelCase   :						str							=   (TFXGLMForCausalLM,) if is_tf_available() else ()
  __lowerCAmelCase   :						Optional[int]							=   (
      {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
  )
  __lowerCAmelCase   :						Dict							=   False
  __lowerCAmelCase   :						Any							=   False
  __lowerCAmelCase   :						Tuple							=   False


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       TFXGLMModelTester(self )
       A_          =       ConfigTester(self ,config_class=snake_case__ ,n_embd=3_7 )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       self.config_tester.run_common_tests()


  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A_          =       TFXGLMModel.from_pretrained(snake_case__ )
            self.assertIsNotNone(snake_case__ )




  @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       super().test_resize_token_embeddings()





@require_tf
class UpperCAmelCase_				( unittest.TestCase ):


  @slow
  def 						__UpperCAmelCase     (							self ,__snake_case=True ):



       """simple docstring"""





       A_          =       TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
       A_          =       tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]] ,dtype=tf.intaa )  # The dog
       # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
       # fmt: off
       A_          =       [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
       # fmt: on
       A_          =       model.generate(snake_case__ ,do_sample=snake_case__ ,num_beams=1 )
       if verify_outputs:
            self.assertListEqual(output_ids[0].numpy().tolist() ,snake_case__ )


  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
       A_          =       TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )

       tf.random.set_seed(0 )
       A_          =       tokenizer('''Today is a nice day and''' ,return_tensors='''tf''' )
       A_          =       tokenized.input_ids
       # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
       with tf.device(''':/CPU:0''' ):
            A_          =       model.generate(snake_case__ ,do_sample=snake_case__ ,seed=[7, 0] )
       A_          =       tokenizer.decode(output_ids[0] ,skip_special_tokens=snake_case__ )

       A_          =       (
           'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
       )
       self.assertEqual(snake_case__ ,snake_case__ )




  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''' )
       A_          =       XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )

       A_          =       'left'

       # use different length sentences to test batching
       A_          =       [
           'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
           'left-padding, such as in batched generation. The output for the sequence below should be the same '
           'regardless of whether left padding is applied or not. When',
           'Hello, my dog is a little',
       ]

       A_          =       tokenizer(snake_case__ ,return_tensors='''tf''' ,padding=snake_case__ )
       A_          =       inputs['input_ids']

       A_          =       model.generate(input_ids=snake_case__ ,attention_mask=inputs['''attention_mask'''] ,max_new_tokens=1_2 )

       A_          =       tokenizer(sentences[0] ,return_tensors='''tf''' ).input_ids
       A_          =       model.generate(input_ids=snake_case__ ,max_new_tokens=1_2 )

       A_          =       tokenizer(sentences[1] ,return_tensors='''tf''' ).input_ids
       A_          =       model.generate(input_ids=snake_case__ ,max_new_tokens=1_2 )

       A_          =       tokenizer.batch_decode(snake_case__ ,skip_special_tokens=snake_case__ )
       A_          =       tokenizer.decode(output_non_padded[0] ,skip_special_tokens=snake_case__ )
       A_          =       tokenizer.decode(output_padded[0] ,skip_special_tokens=snake_case__ )

       A_          =       [
           'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
           'left-padding, such as in batched generation. The output for the sequence below should be the same '
           'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
           'a single',
           'Hello, my dog is a little bit of a shy one, but he is very friendly',
       ]
       self.assertListEqual(snake_case__ ,snake_case__ )
       self.assertListEqual(snake_case__ ,[non_padded_sentence, padded_sentence] )







'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       if len(_A       ) <= 1:
              return [tuple(_A       )]

       _lowerCAmelCase     :				List[str]       =							[]

       def generate(_A ,    _A       ):
              _lowerCAmelCase     :				Any       =							[0] * n
              res.append(tuple(_A       )       )

              _lowerCAmelCase     :				Any       =							0
              while i < n:
                     if c[i] < i:
                            if i % 2 == 0:
                                   _lowerCAmelCase      ,  _lowerCAmelCase     :				Tuple       =							arr[i], arr[0]
                            else:
                                   _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							arr[i], arr[c[i]]
                            res.append(tuple(_A       )       )
                            c[i] += 1
                            _lowerCAmelCase     :				Optional[int]       =							0
                     else:
                            _lowerCAmelCase     :				Any       =							0
                            i += 1

       generate(len(_A       ) ,    _A       )
       return res


if __name__ == "__main__":
       lowerCAmelCase     :  int											=    input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase     :  Optional[int]											=    [int(item) for item in user_input.split(""",""")]
       print(heaps(arr))

