


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   :
			UpperCamelCase        =					XGLMConfig
			UpperCamelCase        =					{}
			UpperCamelCase        =					"gelu"



			def __init__(						self       :  List[str]			,		A       :  str			,		A       :  List[str]=14			,		A       :  List[Any]=7			,		A       :  Dict=True			,		A       :  str=True			,		A       :  Union[str, Any]=True			,		A       :  Any=99			,		A       :  int=32			,		A       :  Union[str, Any]=2			,		A       :  Optional[int]=4			,		A       :  Dict=37			,		A       :  Dict="gelu"			,		A       :  Dict=0.1			,		A       :  List[Any]=0.1			,		A       :  Dict=5_12			,		A       :  int=0.0_2			,		)       ->   Tuple:



						"""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    _lowerCamelCase	(						self       :  Dict)       ->   List[str]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  List[str])       ->   Optional[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    _lowerCamelCase	(						self       :  str)       ->   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=_UpperCAmelCase			,		bos_token_id=self.bos_token_id			,		eos_token_id=self.eos_token_id			,		pad_token_id=self.pad_token_id			,		return_dict=_UpperCAmelCase			,		)



			def    _lowerCamelCase	(						self       :  Dict)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()

						(
						    _UpperCAmelCase			
						)			=       config_and_inputs

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

						return config, inputs_dict







@require_tf
class       __lowerCAmelCase   (      _UpperCAmelCase    ,						_UpperCAmelCase    ,						unittest.TestCase						):
			UpperCamelCase        =					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			UpperCamelCase        =					(TFXGLMForCausalLM,) if is_tf_available() else ()
			UpperCamelCase        =					(
			    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
			)
			UpperCamelCase        =					False
			UpperCamelCase        =					False
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       TFXGLMModelTester(self)
						_UpperCAmelCase			=       ConfigTester(self			,		config_class=_UpperCAmelCase			,		n_embd=37)



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Any:



						"""simple docstring"""






						self.config_tester.run_common_tests()



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



						"""simple docstring"""






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



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



						"""simple docstring"""






						super().test_resize_token_embeddings()







@require_tf
class       __lowerCAmelCase   (      unittest.TestCase						):



			@slow
			def    _lowerCamelCase	(						self       :  Dict			,		A       :  str=True)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M')
						_UpperCAmelCase			=       tf.convert_to_tensor([[2, 2_68, 98_65]]			,		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, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
						# fmt: on
						_UpperCAmelCase			=       model.generate(_UpperCAmelCase			,		do_sample=_UpperCAmelCase			,		num_beams=1)
						if verify_outputs:
									self.assertListEqual(output_ids[0].numpy().tolist()			,		_UpperCAmelCase)



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



						"""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(_UpperCAmelCase			,		do_sample=_UpperCAmelCase			,		seed=[7, 0])
						_UpperCAmelCase			=       tokenizer.decode(output_ids[0]			,		skip_special_tokens=_UpperCAmelCase)

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



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



						"""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(_UpperCAmelCase			,		return_tensors='tf'			,		padding=_UpperCAmelCase)
						_UpperCAmelCase			=       inputs['''input_ids''']

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

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

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

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

						_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(_UpperCAmelCase			,		_UpperCAmelCase)
						self.assertListEqual(_UpperCAmelCase			,		[non_padded_sentence, padded_sentence])





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase__       =	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
							from .tokenization_bertweet import BertweetTokenizer

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
