






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						:			int									=      XGLMConfig
					_A						:			Any									=      {}
					_A						:			Any									=      '''gelu'''



					def __init__(  self				:	int		,     __a				:	Dict		,     __a				:	int=14		,     __a				:	List[Any]=7		,     __a				:	List[Any]=True		,     __a				:	int=True		,     __a				:	Optional[int]=True		,     __a				:	Union[str, Any]=99		,     __a				:	int=32		,     __a				:	Optional[Any]=2		,     __a				:	Optional[int]=4		,     __a				:	Optional[Any]=37		,     __a				:	int="gelu"		,     __a				:	str=0.1		,     __a				:	str=0.1		,     __a				:	int=512		,     __a				:	Dict=0.02		,     )			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     parent
												__lowercase      :							Tuple				=     batch_size
												__lowercase      :							int				=     seq_length
												__lowercase      :							int				=     is_training
												__lowercase      :							Any				=     use_input_mask
												__lowercase      :							Any				=     use_labels
												__lowercase      :							Optional[Any]				=     vocab_size
												__lowercase      :							Tuple				=     d_model
												__lowercase      :							List[str]				=     num_hidden_layers
												__lowercase      :							Any				=     num_attention_heads
												__lowercase      :							List[Any]				=     ffn_dim
												__lowercase      :							Union[str, Any]				=     activation_function
												__lowercase      :							Optional[Any]				=     activation_dropout
												__lowercase      :							Optional[int]				=     attention_dropout
												__lowercase      :							List[str]				=     max_position_embeddings
												__lowercase      :							List[Any]				=     initializer_range
												__lowercase      :							Any				=     None
												__lowercase      :							int				=     0
												__lowercase      :							Dict				=     2
												__lowercase      :							List[Any]				=     1



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  str:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     tf.clip_by_value(
												    ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size							)		,     clip_value_min=0		,     clip_value_max=3							)

												__lowercase      :							int				=     None
												if self.use_input_mask:
																			__lowercase      :							List[str]				=     random_attention_mask([self.batch_size, self.seq_length]							)

												__lowercase      :							Optional[Any]				=     self.get_config()

												__lowercase      :							int				=     floats_tensor([self.num_hidden_layers, self.num_attention_heads]		,     2							)

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												)



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Tuple:


												"""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=__a		,     bos_token_id=self.bos_token_id		,     eos_token_id=self.eos_token_id		,     pad_token_id=self.pad_token_id		,     return_dict=__a		,     )



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							int				=     self.prepare_config_and_inputs()

												(
												    __lowercase			
												)      :							Optional[int]				=     config_and_inputs

												__lowercase      :							Tuple				=     {
												    """input_ids""": input_ids,
												    """head_mask""": head_mask,
												}

												return config, inputs_dict







@require_tf
class 		lowerCAmelCase     ( __a      ,       __a      ,       unittest.TestCase			):

					'''simple docstring'''

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



					def       lowerCAmelCase				(  self				:	Dict							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     TFXGLMModelTester(self							)
												__lowercase      :							int				=     ConfigTester(self		,     config_class=__a		,     n_embd=37							)



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Tuple:


												"""simple docstring"""

												self.config_tester.run_common_tests()



					@slow
					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  str:


												"""simple docstring"""

												for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__lowercase      :							int				=     TFXGLMModel.from_pretrained(__a							)
																			self.assertIsNotNone(__a							)



					@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""							)
					def       lowerCAmelCase				(  self				:	str							)			->  str:


												"""simple docstring"""

												super().test_resize_token_embeddings()







@require_tf
class 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

					@slow
					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Optional[int]=True							)			->  int:


												"""simple docstring"""

												__lowercase      :							int				=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""							)
												__lowercase      :							Tuple				=     tf.convert_to_tensor([[2, 268, 9865]]		,     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
												__lowercase      :							Any				=     [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
												# fmt: on
												__lowercase      :							Union[str, Any]				=     model.generate(__a		,     do_sample=__a		,     num_beams=1							)
												if verify_outputs:
																			self.assertListEqual(output_ids[0].numpy().tolist()		,     __a							)



					@slow
					def       lowerCAmelCase				(  self				:	Any							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""							)
												__lowercase      :							List[Any]				=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""							)

												tf.random.set_seed(0							)
												__lowercase      :							Optional[Any]				=     tokenizer("""Today is a nice day and"""		,     return_tensors="""tf"""							)
												__lowercase      :							str				=     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"""							):
																			__lowercase      :							List[str]				=     model.generate(__a		,     do_sample=__a		,     seed=[7, 0]							)
												__lowercase      :							List[Any]				=     tokenizer.decode(output_ids[0]		,     skip_special_tokens=__a							)

												__lowercase      :							int				=     (
												    """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
												)
												self.assertEqual(__a		,     __a							)



					@slow
					def       lowerCAmelCase				(  self				:	List[str]							)			->  str:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""							)
												__lowercase      :							Dict				=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""							)

												__lowercase      :							Any				=     """left"""

												# use different length sentences to test batching
												__lowercase      :							Any				=     [
												    """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""",
												]

												__lowercase      :							Any				=     tokenizer(__a		,     return_tensors="""tf"""		,     padding=__a							)
												__lowercase      :							Union[str, Any]				=     inputs["""input_ids"""]

												__lowercase      :							Tuple				=     model.generate(input_ids=__a		,     attention_mask=inputs["""attention_mask"""]		,     max_new_tokens=12							)

												__lowercase      :							Dict				=     tokenizer(sentences[0]		,     return_tensors="""tf"""							).input_ids
												__lowercase      :							int				=     model.generate(input_ids=__a		,     max_new_tokens=12							)

												__lowercase      :							int				=     tokenizer(sentences[1]		,     return_tensors="""tf"""							).input_ids
												__lowercase      :							Tuple				=     model.generate(input_ids=__a		,     max_new_tokens=12							)

												__lowercase      :							Union[str, Any]				=     tokenizer.batch_decode(__a		,     skip_special_tokens=__a							)
												__lowercase      :							Optional[Any]				=     tokenizer.decode(output_non_padded[0]		,     skip_special_tokens=__a							)
												__lowercase      :							List[str]				=     tokenizer.decode(output_padded[0]		,     skip_special_tokens=__a							)

												__lowercase      :							Optional[Any]				=     [
												    """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(__a		,     __a							)
												self.assertListEqual(__a		,     [non_padded_sentence, padded_sentence]							)












from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						list[int]      ):
							if not nums:
														return 0
							__lowercase      :							Tuple				=     nums[0]
							__lowercase      :							Tuple				=     0
							for num in nums[1:]:
														__lowercase			,			__lowercase      :							List[str]				=     (
														    max_excluding + num,
														    max(lowerCAmelCase_       ,       lowerCAmelCase_      ),
														)
							return max(lowerCAmelCase_       ,       lowerCAmelCase_      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()