






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 							_lowerCAmelCase:





       """simple docstring"""







       a							:						Optional[int]										=XGLMConfig
       a							:						str										={}
       a							:						Any										='''gelu'''





       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase=1_4    ,     _lowerCamelCase=7    ,     _lowerCamelCase=True    ,     _lowerCamelCase=True    ,     _lowerCamelCase=True    ,     _lowerCamelCase=9_9    ,     _lowerCamelCase=3_2    ,     _lowerCamelCase=2    ,     _lowerCamelCase=4    ,     _lowerCamelCase=3_7    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=5_1_2    ,     _lowerCamelCase=0.0_2    ,     ):
         UpperCamelCase_:   Dict        =							parent
         UpperCamelCase_:   str        =							batch_size
         UpperCamelCase_:   List[str]        =							seq_length
         UpperCamelCase_:   Tuple        =							is_training
         UpperCamelCase_:   Optional[Any]        =							use_input_mask
         UpperCamelCase_:   Any        =							use_labels
         UpperCamelCase_:   List[Any]        =							vocab_size
         UpperCamelCase_:   Optional[Any]        =							d_model
         UpperCamelCase_:   List[str]        =							num_hidden_layers
         UpperCamelCase_:   List[str]        =							num_attention_heads
         UpperCamelCase_:   List[str]        =							ffn_dim
         UpperCamelCase_:   str        =							activation_function
         UpperCamelCase_:   Union[str, Any]        =							activation_dropout
         UpperCamelCase_:   str        =							attention_dropout
         UpperCamelCase_:   str        =							max_position_embeddings
         UpperCamelCase_:   List[str]        =							initializer_range
         UpperCamelCase_:   str        =							None
         UpperCamelCase_:   List[Any]        =							0
         UpperCamelCase_:   str        =							2
         UpperCamelCase_:   Tuple        =							1





       def      _a       (   self				):
         return XGLMConfig.from_pretrained('facebook/xglm-564M'				)





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							tf.clip_by_value(
             ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size				)    ,     clip_value_min=0    ,     clip_value_max=3				)

         UpperCamelCase_:   Dict        =							None
         if self.use_input_mask:
           UpperCamelCase_:   Optional[Any]        =							random_attention_mask([self.batch_size, self.seq_length]				)

         UpperCamelCase_:   Optional[int]        =							self.get_config()

         UpperCamelCase_:   Union[str, Any]        =							floats_tensor([self.num_hidden_layers, self.num_attention_heads]    ,     2				)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )





       def      _a       (   self				):
         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=_lowerCamelCase    ,     bos_token_id=self.bos_token_id    ,     eos_token_id=self.eos_token_id    ,     pad_token_id=self.pad_token_id    ,     return_dict=_lowerCamelCase    ,     )





       def      _a       (   self				):
         UpperCamelCase_:   int        =							self.prepare_config_and_inputs()

         (
             (
             UpperCamelCase_
         ) ,(
             UpperCamelCase_
         ) ,(
             UpperCamelCase_
         ) ,(
             UpperCamelCase_
         ) ,
         ):   List[str]        =							config_and_inputs

         UpperCamelCase_:   Any        =							{
             'input_ids': input_ids,
             'head_mask': head_mask,
         }

         return config, inputs_dict







@require_tf
class 							_lowerCAmelCase(			UpperCAmelCase_				,      UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						str										=(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
       a							:						int										=(TFXGLMForCausalLM,) if is_tf_available() else ()
       a							:						str										=(
           {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
       )
       a							:						Optional[int]										=False
       a							:						Any										=False
       a							:						Optional[int]										=False





       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							TFXGLMModelTester(self				)
         UpperCamelCase_:   Any        =							ConfigTester(self    ,     config_class=_lowerCamelCase    ,     n_embd=3_7				)





       def      _a       (   self				):
         self.config_tester.run_common_tests()





       @slow
       def      _a       (   self				):
         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase_:   Union[str, Any]        =							TFXGLMModel.from_pretrained(_lowerCamelCase				)
           self.assertIsNotNone(_lowerCamelCase				)





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







@require_tf
class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       @slow
       def      _a       (   self    ,     _lowerCamelCase=True				):
         UpperCamelCase_:   Optional[Any]        =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)
         UpperCamelCase_:   int        =							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
         UpperCamelCase_:   List[Any]        =							[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
         UpperCamelCase_:   int        =							model.generate(_lowerCamelCase    ,     do_sample=_lowerCamelCase    ,     num_beams=1				)
         if verify_outputs:
           self.assertListEqual(output_ids[0].numpy().tolist()    ,     _lowerCamelCase				)





       @slow
       def      _a       (   self				):
         UpperCamelCase_:   str        =							XGLMTokenizer.from_pretrained('facebook/xglm-564M'				)
         UpperCamelCase_:   int        =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)

         tf.random.set_seed(0				)
         UpperCamelCase_:   str        =							tokenizer('Today is a nice day and'    ,     return_tensors='tf'				)
         UpperCamelCase_:   int        =							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'				):
           UpperCamelCase_:   str        =							model.generate(_lowerCamelCase    ,     do_sample=_lowerCamelCase    ,     seed=[7, 0]				)
         UpperCamelCase_:   Tuple        =							tokenizer.decode(output_ids[0]    ,     skip_special_tokens=_lowerCamelCase				)

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





       @slow
       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)
         UpperCamelCase_:   List[Any]        =							XGLMTokenizer.from_pretrained('facebook/xglm-564M'				)

         UpperCamelCase_:   Optional[int]        =							'left'

         # use different length sentences to test batching
         UpperCamelCase_:   str        =							[
             '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',
         ]

         UpperCamelCase_:   List[str]        =							tokenizer(_lowerCamelCase    ,     return_tensors='tf'    ,     padding=_lowerCamelCase				)
         UpperCamelCase_:   Optional[Any]        =							inputs['input_ids']

         UpperCamelCase_:   Optional[Any]        =							model.generate(input_ids=_lowerCamelCase    ,     attention_mask=inputs['attention_mask']    ,     max_new_tokens=1_2				)

         UpperCamelCase_:   Dict        =							tokenizer(sentences[0]    ,     return_tensors='tf'				).input_ids
         UpperCamelCase_:   int        =							model.generate(input_ids=_lowerCamelCase    ,     max_new_tokens=1_2				)

         UpperCamelCase_:   Tuple        =							tokenizer(sentences[1]    ,     return_tensors='tf'				).input_ids
         UpperCamelCase_:   str        =							model.generate(input_ids=_lowerCamelCase    ,     max_new_tokens=1_2				)

         UpperCamelCase_:   Tuple        =							tokenizer.batch_decode(_lowerCamelCase    ,     skip_special_tokens=_lowerCamelCase				)
         UpperCamelCase_:   Optional[Any]        =							tokenizer.decode(output_non_padded[0]    ,     skip_special_tokens=_lowerCamelCase				)
         UpperCamelCase_:   int        =							tokenizer.decode(output_padded[0]    ,     skip_special_tokens=_lowerCamelCase				)

         UpperCamelCase_:   Dict        =							[
             '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(_lowerCamelCase    ,     _lowerCamelCase				)
         self.assertListEqual(_lowerCamelCase    ,     [non_padded_sentence, padded_sentence]				)

import unittest

from knapsack import knapsack as k
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     Optional[int]			)			->	str:
					lowercase_	    =   0
					lowercase_	    =   [0]
					lowercase_	    =   [0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)

					lowercase_	    =   [6_0]
					lowercase_	    =   [1_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)
	def 		_lowercase    (			self    :     int			)			->	str:
					lowercase_	    =   3
					lowercase_	    =   [1, 2, 3]
					lowercase_	    =   [3, 2, 1]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				5			)




	def 		_lowercase    (			self    :     str			)			->	Tuple:
					lowercase_	    =   5_0
					lowercase_	    =   [6_0, 1_0_0, 1_2_0]
					lowercase_	    =   [1_0, 2_0, 3_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				2_2_0			)


if __name__ == "__main__":
						unittest.main()




