
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__  :
			_SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				XGLMConfig
			_SCREAMING_SNAKE_CASE				:							List[str]     	=				{}
			_SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				"gelu"
			def __init__(self			: Any ,    snake_case_			: Dict ,    snake_case_			: str=1_4 ,    snake_case_			: int=7 ,    snake_case_			: str=True ,    snake_case_			: Tuple=True ,    snake_case_			: Dict=True ,    snake_case_			: Optional[Any]=9_9 ,    snake_case_			: str=3_2 ,    snake_case_			: int=2 ,    snake_case_			: Any=4 ,    snake_case_			: Optional[Any]=3_7 ,    snake_case_			: List[Any]="gelu" ,    snake_case_			: Union[str, Any]=0.1 ,    snake_case_			: Any=0.1 ,    snake_case_			: Union[str, Any]=5_1_2 ,    snake_case_			: Any=0.02 ,    ):
				__a			:  Optional[Any]		    =  parent
				__a			:  Tuple		    =  batch_size
				__a			:  Union[str, Any]		    =  seq_length
				__a			:  Union[str, Any]		    =  is_training
				__a			:  List[str]		    =  use_input_mask
				__a			:  Optional[int]		    =  use_labels
				__a			:  Union[str, Any]		    =  vocab_size
				__a			:  str		    =  d_model
				__a			:  List[str]		    =  num_hidden_layers
				__a			:  Any		    =  num_attention_heads
				__a			:  Tuple		    =  ffn_dim
				__a			:  List[Any]		    =  activation_function
				__a			:  str		    =  activation_dropout
				__a			:  Optional[int]		    =  attention_dropout
				__a			:  Optional[Any]		    =  max_position_embeddings
				__a			:  int		    =  initializer_range
				__a			:  Optional[Any]		    =  None
				__a			:  str		    =  0
				__a			:  Tuple		    =  2
				__a			:  List[str]		    =  1
			def 				lowerCAmelCase      (self			: str       ):
				return XGLMConfig.from_pretrained('''facebook/xglm-564M'''       )
			def 				lowerCAmelCase      (self			: Optional[int]       ):
				__a			:  Optional[Any]		    =  tf.clip_by_value(
				    ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size       ) ,    clip_value_min=0 ,    clip_value_max=3       )

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

				__a			:  List[Any]		    =  self.get_config()

				__a			:  List[str]		    =  floats_tensor([self.num_hidden_layers, self.num_attention_heads] ,    2       )

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				)
			def 				lowerCAmelCase      (self			: 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=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 				lowerCAmelCase      (self			: Union[str, Any]       ):
				__a			:  Optional[Any]		    =  self.prepare_config_and_inputs()

				(
				    (
				    __a    
				)    ,	(
				    __a    
				)    ,	(
				    __a    
				)    ,	(
				    __a    
				)    ,	    
				)			:  Optional[int]		    =  config_and_inputs

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

				return config, inputs_dict





@require_tf
class 		UpperCamelCase__  (	__lowercase   ,__lowercase   ,unittest.TestCase   ):
			_SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			_SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				(TFXGLMForCausalLM,) if is_tf_available() else ()
			_SCREAMING_SNAKE_CASE				:							List[Any]     	=				(
			    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
			)
			_SCREAMING_SNAKE_CASE				:							List[Any]     	=				False
			_SCREAMING_SNAKE_CASE				:							int     	=				False
			_SCREAMING_SNAKE_CASE				:							str     	=				False
			def 				lowerCAmelCase      (self			: Optional[Any]       ):
				__a			:  Union[str, Any]		    =  TFXGLMModelTester(self       )
				__a			:  Optional[int]		    =  ConfigTester(self ,    config_class=lowerCamelCase_ ,    n_embd=3_7       )
			def 				lowerCAmelCase      (self			: Dict       ):
				self.config_tester.run_common_tests()
			@slow
			def 				lowerCAmelCase      (self			: Optional[Any]       ):

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







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





@require_tf
class 		UpperCamelCase__  (	unittest.TestCase   ):
			@slow
			def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: Dict=True       ):
				__a			:  Optional[int]		    =  TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )
				__a			:  Dict		    =  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			:  Optional[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
				__a			:  Tuple		    =  model.generate(lowerCamelCase_ ,    do_sample=lowerCamelCase_ ,    num_beams=1       )
				if verify_outputs:
					self.assertListEqual(output_ids[0].numpy().tolist() ,    lowerCamelCase_       )
			@slow
			def 				lowerCAmelCase      (self			: str       ):
				__a			:  Dict		    =  XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''       )
				__a			:  Optional[Any]		    =  TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )

				tf.random.set_seed(0       )
				__a			:  Union[str, Any]		    =  tokenizer('''Today is a nice day and''' ,    return_tensors='''tf'''       )
				__a			:  List[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'''       ):
					__a			:  Any		    =  model.generate(lowerCamelCase_ ,    do_sample=lowerCamelCase_ ,    seed=[7, 0]       )
				__a			:  int		    =  tokenizer.decode(output_ids[0] ,    skip_special_tokens=lowerCamelCase_       )

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







			@slow
			def 				lowerCAmelCase      (self			: Tuple       ):
				__a			:  Any		    =  TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )
				__a			:  Tuple		    =  XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''       )

				__a			:  Optional[int]		    =  '''left'''

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

				__a			:  str		    =  tokenizer(lowerCamelCase_ ,    return_tensors='''tf''' ,    padding=lowerCamelCase_       )
				__a			:  List[str]		    =  inputs['''input_ids''']

				__a			:  Union[str, Any]		    =  model.generate(input_ids=lowerCamelCase_ ,    attention_mask=inputs['''attention_mask'''] ,    max_new_tokens=1_2       )

				__a			:  List[Any]		    =  tokenizer(sentences[0] ,    return_tensors='''tf'''       ).input_ids
				__a			:  Union[str, Any]		    =  model.generate(input_ids=lowerCamelCase_ ,    max_new_tokens=1_2       )

				__a			:  List[str]		    =  tokenizer(sentences[1] ,    return_tensors='''tf'''       ).input_ids
				__a			:  Optional[int]		    =  model.generate(input_ids=lowerCamelCase_ ,    max_new_tokens=1_2       )

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

				__a			:  List[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 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 transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				CustomTokenizer
   pass
