






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 		__a				:
		_a     :  Optional[Any]           =     XGLMConfig
		_a     :  Optional[Any]           =     {}
		_a     :  Union[str, Any]           =     'gelu'
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=14      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=99      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=37      ,   _SCREAMING_SNAKE_CASE="gelu"      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=512      ,   _SCREAMING_SNAKE_CASE=0.02      ,   )			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      seq_length
									_UpperCAmelCase					=      is_training
									_UpperCAmelCase					=      use_input_mask
									_UpperCAmelCase					=      use_labels
									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      d_model
									_UpperCAmelCase					=      num_hidden_layers
									_UpperCAmelCase					=      num_attention_heads
									_UpperCAmelCase					=      ffn_dim
									_UpperCAmelCase					=      activation_function
									_UpperCAmelCase					=      activation_dropout
									_UpperCAmelCase					=      attention_dropout
									_UpperCAmelCase					=      max_position_embeddings
									_UpperCAmelCase					=      initializer_range
									_UpperCAmelCase					=      None
									_UpperCAmelCase					=      0
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      1
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





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




									"""simple docstring"""





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

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

									_UpperCAmelCase					=      self.get_config()

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

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									)
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""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=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      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      self.prepare_config_and_inputs()

									(
									    (
									    _UpperCAmelCase    
									)		,	(
									    _UpperCAmelCase    
									)		,	(
									    _UpperCAmelCase    
									)		,	(
									    _UpperCAmelCase    
									)		,	    
									)					=      config_and_inputs

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

									return config, inputs_dict



@require_tf
class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  List[Any]           =     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
		_a     :  List[Any]           =     (TFXGLMForCausalLM,) if is_tf_available() else ()
		_a     :  Dict           =     (
		    {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
		)
		_a     :  int           =     False
		_a     :  List[str]           =     False
		_a     :  Optional[Any]           =     False
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      TFXGLMModelTester(self						)
									_UpperCAmelCase					=      ConfigTester(self      ,   config_class=lowerCamelCase_      ,   n_embd=37						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		@slow
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





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






		@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'						)
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									super().test_resize_token_embeddings()



@require_tf
class 		__a				(    unittest.TestCase      ):
		@slow
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=True						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'						)
									_UpperCAmelCase					=      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
									_UpperCAmelCase					=      [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
									# fmt: on
									_UpperCAmelCase					=      model.generate(lowerCamelCase_      ,   do_sample=lowerCamelCase_      ,   num_beams=1						)
									if verify_outputs:
																self.assertListEqual(output_ids[0].numpy().tolist()      ,   lowerCamelCase_						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





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

									tf.random.set_seed(0						)
									_UpperCAmelCase					=      tokenizer('Today is a nice day and'      ,   return_tensors='tf'						)
									_UpperCAmelCase					=      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					=      model.generate(lowerCamelCase_      ,   do_sample=lowerCamelCase_      ,   seed=[7, 0]						)
									_UpperCAmelCase					=      tokenizer.decode(output_ids[0]      ,   skip_special_tokens=lowerCamelCase_						)

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






		@slow
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      'left'

									# use different length sentences to test batching
									_UpperCAmelCase					=      [
									    '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					=      tokenizer(lowerCamelCase_      ,   return_tensors='tf'      ,   padding=lowerCamelCase_						)
									_UpperCAmelCase					=      inputs['input_ids']

									_UpperCAmelCase					=      model.generate(input_ids=lowerCamelCase_      ,   attention_mask=inputs['attention_mask']      ,   max_new_tokens=12						)

									_UpperCAmelCase					=      tokenizer(sentences[0]      ,   return_tensors='tf'						).input_ids
									_UpperCAmelCase					=      model.generate(input_ids=lowerCamelCase_      ,   max_new_tokens=12						)

									_UpperCAmelCase					=      tokenizer(sentences[1]      ,   return_tensors='tf'						).input_ids
									_UpperCAmelCase					=      model.generate(input_ids=lowerCamelCase_      ,   max_new_tokens=12						)

									_UpperCAmelCase					=      tokenizer.batch_decode(lowerCamelCase_      ,   skip_special_tokens=lowerCamelCase_						)
									_UpperCAmelCase					=      tokenizer.decode(output_non_padded[0]      ,   skip_special_tokens=lowerCamelCase_						)
									_UpperCAmelCase					=      tokenizer.decode(output_padded[0]      ,   skip_special_tokens=lowerCamelCase_						)

									_UpperCAmelCase					=      [
									    '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]						)




from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt

def 	lowerCAmelCase__			(      a__:		Sequence[float]      ,  a__:		int      ,  a__:		int	)  ->		tuple[int | None, int | None, float]:


							'''simple docstring'''





							if not arr:
														return None, None, 0
							if low == high:
														return low, high, arr[low]

							_UpperCAmelCase					=      (low + high) // 2
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_subarray(a__      ,  a__      ,  a__	)
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_subarray(a__      ,  mid + 1      ,  a__	)
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_cross_sum(a__      ,  a__      ,  a__      ,  a__	)
							if left_sum >= right_sum and left_sum >= cross_sum:
														return left_low, left_high, left_sum
							elif right_sum >= left_sum and right_sum >= cross_sum:
														return right_low, right_high, right_sum
							return cross_left, cross_right, cross_sum

def 	lowerCAmelCase__			(      a__:		Sequence[float]      ,  a__:		int      ,  a__:		int      ,  a__:		int	)  ->		tuple[int, int, float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      float('-inf'	), -1
							_UpperCAmelCase		,	_UpperCAmelCase					=      float('-inf'	), -1

							_UpperCAmelCase					=      0
							for i in range(a__      ,  low - 1      ,  -1	):
														summ += arr[i]
														if summ > left_sum:
																					_UpperCAmelCase					=      summ
																					_UpperCAmelCase					=      i

							_UpperCAmelCase					=      0
							for i in range(mid + 1      ,  high + 1	):
														summ += arr[i]
														if summ > right_sum:
																					_UpperCAmelCase					=      summ
																					_UpperCAmelCase					=      i

							return max_left, max_right, (left_sum + right_sum)

def 	lowerCAmelCase__			(      a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      [randint(1      ,  a__	) for _ in range(a__	)]
							_UpperCAmelCase					=      time.time()
							max_subarray(a__      ,  0      ,  input_size - 1	)
							_UpperCAmelCase					=      time.time()
							return end - start

def 	lowerCAmelCase__			(      )  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
							_UpperCAmelCase					=      [time_max_subarray(a__	) for input_size in input_sizes]
							print('No of Inputs\t\tTime Taken'	)
							for input_size, runtime in zip(a__      ,  a__	):
														print(a__      ,  '\t\t'      ,  a__	)
							plt.plot(a__      ,  a__	)
							plt.xlabel('Number of Inputs'	)
							plt.ylabel('Time taken in seconds'	)
							plt.show()


if __name__ == "__main__":
		from doctest import testmod

		testmod()
