




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 :
	SCREAMING_SNAKE_CASE       =       XGLMConfig
	SCREAMING_SNAKE_CASE       =       {}
	SCREAMING_SNAKE_CASE       =       "gelu"



	def __init__(						self							:    Tuple				,	snake_case_							:    Tuple				,	snake_case_							:    Any=14				,	snake_case_							:    Tuple=7				,	snake_case_							:    Dict=True				,	snake_case_							:    Union[str, Any]=True				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    Tuple=99				,	snake_case_							:    int=32				,	snake_case_							:    str=2				,	snake_case_							:    int=4				,	snake_case_							:    Dict=37				,	snake_case_							:    Tuple="gelu"				,	snake_case_							:    Any=0.1				,	snake_case_							:    Optional[int]=0.1				,	snake_case_							:    Optional[Any]=5_12				,	snake_case_							:    int=0.0_2				,	)->							List[str]:
						__lowerCAmelCase	   =parent
						__lowerCAmelCase	   =batch_size
						__lowerCAmelCase	   =seq_length
						__lowerCAmelCase	   =is_training
						__lowerCAmelCase	   =use_input_mask
						__lowerCAmelCase	   =use_labels
						__lowerCAmelCase	   =vocab_size
						__lowerCAmelCase	   =d_model
						__lowerCAmelCase	   =num_hidden_layers
						__lowerCAmelCase	   =num_attention_heads
						__lowerCAmelCase	   =ffn_dim
						__lowerCAmelCase	   =activation_function
						__lowerCAmelCase	   =activation_dropout
						__lowerCAmelCase	   =attention_dropout
						__lowerCAmelCase	   =max_position_embeddings
						__lowerCAmelCase	   =initializer_range
						__lowerCAmelCase	   =None
						__lowerCAmelCase	   =0
						__lowerCAmelCase	   =2
						__lowerCAmelCase	   =1



	def 		UpperCamelCase							(						self							:    Any)->							List[Any]:
						return XGLMConfig.from_pretrained("""facebook/xglm-564M""")



	def 		UpperCamelCase							(						self							:    Optional[Any])->							str:
						__lowerCAmelCase	   =tf.clip_by_value(
						    ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size)				,	clip_value_min=0				,	clip_value_max=3)

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

						__lowerCAmelCase	   =self.get_config()

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

						return (
						    config,
						    input_ids,
						    input_mask,
						    head_mask,
						)



	def 		UpperCamelCase							(						self							:    Tuple)->							int:
						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=snake_case_				,	bos_token_id=self.bos_token_id				,	eos_token_id=self.eos_token_id				,	pad_token_id=self.pad_token_id				,	return_dict=snake_case_				,	)



	def 		UpperCamelCase							(						self							:    str)->							str:
						__lowerCAmelCase	   =self.prepare_config_and_inputs()

						(
						    (
						    __lowerCAmelCase	
						)   ,	(
						    __lowerCAmelCase	
						)   ,	(
						    __lowerCAmelCase	
						)   ,	(
						    __lowerCAmelCase	
						)   ,		
						)	   =config_and_inputs

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

						return config, inputs_dict






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



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							str:
						__lowerCAmelCase	   =TFXGLMModelTester(self)
						__lowerCAmelCase	   =ConfigTester(self				,	config_class=snake_case_				,	n_embd=37)



	def 		UpperCamelCase							(						self							:    Tuple)->							str:
						self.config_tester.run_common_tests()



	@slow
	def 		UpperCamelCase							(						self							:    str)->							Tuple:
						for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCAmelCase	   =TFXGLMModel.from_pretrained(snake_case_)
											self.assertIsNotNone(snake_case_)



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






@require_tf
class 						__a (     unittest.TestCase				):



	@slow
	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Optional[int]=True)->							Tuple:
						__lowerCAmelCase	   =TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
						__lowerCAmelCase	   =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
						__lowerCAmelCase	   =[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
						__lowerCAmelCase	   =model.generate(snake_case_				,	do_sample=snake_case_				,	num_beams=1)
						if verify_outputs:
											self.assertListEqual(output_ids[0].numpy().tolist()				,	snake_case_)



	@slow
	def 		UpperCamelCase							(						self							:    Union[str, Any])->							List[str]:
						__lowerCAmelCase	   =XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")
						__lowerCAmelCase	   =TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")

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

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



	@slow
	def 		UpperCamelCase							(						self							:    Dict)->							str:
						__lowerCAmelCase	   =TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
						__lowerCAmelCase	   =XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")

						__lowerCAmelCase	   ="""left"""

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

						__lowerCAmelCase	   =tokenizer(snake_case_				,	return_tensors="""tf"""				,	padding=snake_case_)
						__lowerCAmelCase	   =inputs["""input_ids"""]

						__lowerCAmelCase	   =model.generate(input_ids=snake_case_				,	attention_mask=inputs["""attention_mask"""]				,	max_new_tokens=12)

						__lowerCAmelCase	   =tokenizer(sentences[0]				,	return_tensors="""tf""").input_ids
						__lowerCAmelCase	   =model.generate(input_ids=snake_case_				,	max_new_tokens=12)

						__lowerCAmelCase	   =tokenizer(sentences[1]				,	return_tensors="""tf""").input_ids
						__lowerCAmelCase	   =model.generate(input_ids=snake_case_				,	max_new_tokens=12)

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

						__lowerCAmelCase	   =[
						    """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(snake_case_				,	snake_case_)
						self.assertListEqual(snake_case_				,	[non_padded_sentence, padded_sentence])









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list				,   __lowerCamelCase	:    int				,   __lowerCamelCase	:    int = 0				,   __lowerCamelCase	:    int = 0	)  ->						int:
					__lowerCAmelCase	   =right or len(__lowerCamelCase	) - 1
					if left > right:
										return -1
					elif list_data[left] == key:
										return left
					elif list_data[right] == key:
										return right
					else:
										return search(__lowerCamelCase				,   __lowerCamelCase				,   left + 1				,   right - 1	)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




