"""simple docstring"""







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			:
	_snake_case     =  XGLMConfig
	_snake_case     =  {}
	_snake_case     =  'gelu'







	def __init__( self    :       Any			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       str=1_4			,					lowerCamelCase_    :       Optional[Any]=7			,					lowerCamelCase_    :       Any=True			,					lowerCamelCase_    :       Optional[Any]=True			,					lowerCamelCase_    :       int=True			,					lowerCamelCase_    :       Dict=9_9			,					lowerCamelCase_    :       str=3_2			,					lowerCamelCase_    :       str=2			,					lowerCamelCase_    :       Union[str, Any]=4			,					lowerCamelCase_    :       Tuple=3_7			,					lowerCamelCase_    :       Tuple="gelu"			,					lowerCamelCase_    :       Optional[Any]=0.1			,					lowerCamelCase_    :       Dict=0.1			,					lowerCamelCase_    :       Optional[int]=5_1_2			,					lowerCamelCase_    :       Optional[Any]=0.02			,					):

		"""simple docstring"""






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







	def 							__UpperCAmelCase					( self    :       str						):

		"""simple docstring"""






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







	def 							__UpperCAmelCase					( self    :       int						):

		"""simple docstring"""






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

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

		_lowercase :    str							=							self.get_config()

		_lowercase :    List[Any]							=							floats_tensor([self.num_hidden_layers, self.num_attention_heads]			,					2						)

		return (
		    config,
		    input_ids,
		    input_mask,
		    head_mask,
		)







	def 							__UpperCAmelCase					( self    :       Union[str, 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=_lowercase			,					bos_token_id=self.bos_token_id			,					eos_token_id=self.eos_token_id			,					pad_token_id=self.pad_token_id			,					return_dict=_lowercase			,					)







	def 							__UpperCAmelCase					( self    :       Any						):

		"""simple docstring"""






		_lowercase :    str							=							self.prepare_config_and_inputs()

		(
		    _lowercase      
		) :    Dict							=							config_and_inputs

		_lowercase :    Optional[Any]							=							{
		    'input_ids': input_ids,
		    'head_mask': head_mask,
		}

		return config, inputs_dict



@require_tf
class  _lowerCamelCase			(UpperCAmelCase_						,							UpperCAmelCase_						,							unittest.TestCase     ):
	_snake_case     =  (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
	_snake_case     =  (TFXGLMForCausalLM,) if is_tf_available() else ()
	_snake_case     =  (
	    {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
	)
	_snake_case     =  False
	_snake_case     =  False
	_snake_case     =  False







	def 							__UpperCAmelCase					( self    :       Dict						):

		"""simple docstring"""






		_lowercase :    Optional[Any]							=							TFXGLMModelTester(self						)
		_lowercase :    List[str]							=							ConfigTester(self			,					config_class=_lowercase			,					n_embd=3_7						)







	def 							__UpperCAmelCase					( self    :       int						):

		"""simple docstring"""






		self.config_tester.run_common_tests()







	@slow
	def 							__UpperCAmelCase					( self    :       Dict						):

		"""simple docstring"""






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







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

		"""simple docstring"""






		super().test_resize_token_embeddings()



@require_tf
class  _lowerCamelCase			(unittest.TestCase     ):







	@slow
	def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       Any=True						):

		"""simple docstring"""






		_lowercase :    List[str]							=							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'						)
		_lowercase :    Tuple							=							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
		_lowercase :    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
		_lowercase :    Tuple							=							model.generate(_lowercase			,					do_sample=_lowercase			,					num_beams=1						)
		if verify_outputs:
			self.assertListEqual(output_ids[0].numpy().tolist()			,					_lowercase						)







	@slow
	def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

		"""simple docstring"""






		_lowercase :    int							=							XGLMTokenizer.from_pretrained('facebook/xglm-564M'						)
		_lowercase :    Dict							=							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'						)

		tf.random.set_seed(0						)
		_lowercase :    Any							=							tokenizer('Today is a nice day and'			,					return_tensors='tf'						)
		_lowercase :    Any							=							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(_lowercase			,					do_sample=_lowercase			,					seed=[7, 0]						)
		_lowercase :    Optional[Any]							=							tokenizer.decode(output_ids[0]			,					skip_special_tokens=_lowercase						)

		_lowercase :    Optional[Any]							=							(
		    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
		)
		self.assertEqual(_lowercase			,					_lowercase						)







	@slow
	def 							__UpperCAmelCase					( self    :       int						):

		"""simple docstring"""






		_lowercase :    Optional[Any]							=							TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'						)
		_lowercase :    Optional[Any]							=							XGLMTokenizer.from_pretrained('facebook/xglm-564M'						)

		_lowercase :    str							=							'left'

		# use different length sentences to test batching
		_lowercase :    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',
		]

		_lowercase :    List[Any]							=							tokenizer(_lowercase			,					return_tensors='tf'			,					padding=_lowercase						)
		_lowercase :    Tuple							=							inputs['input_ids']

		_lowercase :    Optional[int]							=							model.generate(input_ids=_lowercase			,					attention_mask=inputs['attention_mask']			,					max_new_tokens=1_2						)

		_lowercase :    Optional[Any]							=							tokenizer(sentences[0]			,					return_tensors='tf'						).input_ids
		_lowercase :    List[Any]							=							model.generate(input_ids=_lowercase			,					max_new_tokens=1_2						)

		_lowercase :    List[Any]							=							tokenizer(sentences[1]			,					return_tensors='tf'						).input_ids
		_lowercase :    Tuple							=							model.generate(input_ids=_lowercase			,					max_new_tokens=1_2						)

		_lowercase :    Union[str, Any]							=							tokenizer.batch_decode(_lowercase			,					skip_special_tokens=_lowercase						)
		_lowercase :    Optional[Any]							=							tokenizer.decode(output_non_padded[0]			,					skip_special_tokens=_lowercase						)
		_lowercase :    Tuple							=							tokenizer.decode(output_padded[0]			,					skip_special_tokens=_lowercase						)

		_lowercase :    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(_lowercase			,					_lowercase						)
		self.assertListEqual(_lowercase			,					[non_padded_sentence, padded_sentence]						)




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    str							=							[0 for i in range(len(__UpperCAmelCase			)			)]

	# initialize interval's left pointer and right pointer
	_lowercase				,      _lowercase :    str							=							0, 0

	for i in range(1     ,len(__UpperCAmelCase			)			):
		# case when current index is inside the interval
		if i <= right_pointer:
			_lowercase :    Union[str, Any]							=							min(right_pointer - i + 1     ,z_result[i - left_pointer]			)
			_lowercase :    Any							=							min_edge

		while go_next(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):
			z_result[i] += 1

		# if new index's result gives us more right interval,
		# we've to update left_pointer and right_pointer
		if i + z_result[i] - 1 > right_pointer:
			_lowercase				,      _lowercase :    Optional[int]							=							i, i + z_result[i] - 1

	return z_result


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	return i + z_result[i] < len(__UpperCAmelCase			) and s[z_result[i]] == s[i + z_result[i]]


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							0
	# concatenate 'pattern' and 'input_str' and call z_function
	# with concatenated string
	_lowercase :    List[str]							=							z_function(pattern + input_str			)

	for val in z_result:
		# if value is greater then length of the pattern string
		# that means this index is starting position of substring
		# which is equal to pattern string
		if val >= len(__UpperCAmelCase			):
			answer += 1

	return answer


if __name__ == "__main__":
						import doctest

						doctest.testmod()



