





'''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       UpperCamelCase__  :
	lowerCAmelCase__					:			Union[str, Any]								=						XGLMConfig
	lowerCAmelCase__					:			Union[str, Any]								=						{}
	lowerCAmelCase__					:			Dict								=						"gelu"







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




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = seq_length
				a__     = is_training
				a__     = use_input_mask
				a__     = use_labels
				a__     = vocab_size
				a__     = d_model
				a__     = num_hidden_layers
				a__     = num_attention_heads
				a__     = ffn_dim
				a__     = activation_function
				a__     = activation_dropout
				a__     = attention_dropout
				a__     = max_position_embeddings
				a__     = initializer_range
				a__     = None
				a__     = 0
				a__     = 2
				a__     = 1







	def 						__a   (				self			:   List[Any]		):




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







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = tf.clip_by_value(
				    ids_tensor([self.batch_size, self.seq_length] , self.vocab_size		) , clip_value_min=0 , clip_value_max=3		)

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

				a__     = self.get_config()

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

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				)







	def 						__a   (				self			:   List[str]		):




				'''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 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = self.prepare_config_and_inputs()

				(
				    (
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				
				)     = config_and_inputs

				a__     = {
				    "input_ids": input_ids,
				    "head_mask": head_mask,
				}

				return config, inputs_dict






@require_tf
class       UpperCamelCase__  (		__lowerCAmelCase	,__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			List[str]								=						(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
	lowerCAmelCase__					:			str								=						(TFXGLMForCausalLM,) if is_tf_available() else ()
	lowerCAmelCase__					:			Tuple								=						(
	    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
	)
	lowerCAmelCase__					:			Tuple								=						False
	lowerCAmelCase__					:			Optional[Any]								=						False
	lowerCAmelCase__					:			int								=						False







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = TFXGLMModelTester(self		)
				a__     = ConfigTester(self , config_class=lowerCamelCase , n_embd=3_7		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				self.config_tester.run_common_tests()







	@slow
	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							a__     = TFXGLMModel.from_pretrained(lowerCamelCase		)
							self.assertIsNotNone(lowerCamelCase		)







	@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."		)
	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				super().test_resize_token_embeddings()






@require_tf
class       UpperCamelCase__  (		unittest.TestCase     ):







	@slow
	def 						__a   (				self			:   Dict , lowerCamelCase			:   int=True		):




				'''simple docstring'''
				a__     = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
				a__     = 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
				a__     = [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
				a__     = model.generate(lowerCamelCase , do_sample=lowerCamelCase , num_beams=1		)
				if verify_outputs:
							self.assertListEqual(output_ids[0].numpy().tolist() , lowerCamelCase		)







	@slow
	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)
				a__     = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)

				tf.random.set_seed(0		)
				a__     = tokenizer("Today is a nice day and" , return_tensors="tf"		)
				a__     = 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"		):
							a__     = model.generate(lowerCamelCase , do_sample=lowerCamelCase , seed=[7, 0]		)
				a__     = tokenizer.decode(output_ids[0] , skip_special_tokens=lowerCamelCase		)

				a__     = (
				    "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			:   Tuple		):




				'''simple docstring'''
				a__     = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
				a__     = XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)

				a__     = "left"

				# use different length sentences to test batching
				a__     = [
				    "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",
				]

				a__     = tokenizer(lowerCamelCase , return_tensors="tf" , padding=lowerCamelCase		)
				a__     = inputs["input_ids"]

				a__     = model.generate(input_ids=lowerCamelCase , attention_mask=inputs["attention_mask"] , max_new_tokens=1_2		)

				a__     = tokenizer(sentences[0] , return_tensors="tf"		).input_ids
				a__     = model.generate(input_ids=lowerCamelCase , max_new_tokens=1_2		)

				a__     = tokenizer(sentences[1] , return_tensors="tf"		).input_ids
				a__     = model.generate(input_ids=lowerCamelCase , max_new_tokens=1_2		)

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

				a__     = [
				    "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]		)










'''simple docstring'''


import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script






@require_cpu
class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				debug_launcher(test_script.main		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				debug_launcher(test_ops.main		)



