

'''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			:



   '''simple docstring'''






   __snake_case        =	XGLMConfig
   __snake_case        =	{}
   __snake_case        =	'gelu'


   def __init__(				self	:						str		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						List[str]=14		,      lowerCAmelCase_	:						Optional[Any]=7		,      lowerCAmelCase_	:						List[Any]=True		,      lowerCAmelCase_	:						Any=True		,      lowerCAmelCase_	:						Tuple=True		,      lowerCAmelCase_	:						Union[str, Any]=99		,      lowerCAmelCase_	:						Union[str, Any]=32		,      lowerCAmelCase_	:						Dict=2		,      lowerCAmelCase_	:						int=4		,      lowerCAmelCase_	:						int=37		,      lowerCAmelCase_	:						int="gelu"		,      lowerCAmelCase_	:						Optional[Any]=0.1		,      lowerCAmelCase_	:						Optional[int]=0.1		,      lowerCAmelCase_	:						Any=5_12		,      lowerCAmelCase_	:						List[Any]=0.02		,      )      ->		Optional[Any]:





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


   def lowercase__						(				self	:						Any			)      ->		List[str]:





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


   def lowercase__						(				self	:						Any			)      ->		str:





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

         A__					:       List[str]     =None
         if self.use_input_mask:
               A__					:       int     =random_attention_mask([self.batch_size, self.seq_length]			)

         A__					:       Dict     =self.get_config()

         A__					:       Optional[int]     =floats_tensor([self.num_hidden_layers, self.num_attention_heads]		,      2			)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )


   def lowercase__						(				self	:						Optional[Any]			)      ->		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 lowercase__						(				self	:						Optional[Any]			)      ->		List[str]:





         '''simple docstring'''
         A__					:       Tuple     =self.prepare_config_and_inputs()

         (
             A__						
         )					:       Union[str, Any]     =config_and_inputs

         A__					:       Optional[int]     ={
             """input_ids""": input_ids,
             """head_mask""": head_mask,
         }

         return config, inputs_dict



@require_tf
class lowerCamelCase			(  lowercase_	,						lowercase_	,						unittest.TestCase						):



   '''simple docstring'''






   __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 lowercase__						(				self	:						Any			)      ->		int:





         '''simple docstring'''
         A__					:       Union[str, Any]     =TFXGLMModelTester(self			)
         A__					:       List[str]     =ConfigTester(self		,      config_class=lowerCAmelCase_		,      n_embd=37			)


   def lowercase__						(				self	:						List[str]			)      ->		int:





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


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





         '''simple docstring'''
         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A__					:       int     =TFXGLMModel.from_pretrained(lowerCAmelCase_			)
               self.assertIsNotNone(lowerCAmelCase_			)



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





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



@require_tf
class lowerCamelCase			(  unittest.TestCase						):



   '''simple docstring'''






   @slow
   def lowercase__						(				self	:						Optional[int]		,      lowerCAmelCase_	:						Optional[Any]=True			)      ->		int:





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


   @slow
   def lowercase__						(				self	:						List[Any]			)      ->		int:





         '''simple docstring'''
         A__					:       List[str]     =XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""			)
         A__					:       List[Any]     =TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)

         tf.random.set_seed(0			)
         A__					:       Dict     =tokenizer("""Today is a nice day and"""		,      return_tensors="""tf"""			)
         A__					:       int     =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__					:       List[str]     =model.generate(lowerCAmelCase_		,      do_sample=lowerCAmelCase_		,      seed=[7, 0]			)
         A__					:       List[str]     =tokenizer.decode(output_ids[0]		,      skip_special_tokens=lowerCAmelCase_			)

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



   @slow
   def lowercase__						(				self	:						Union[str, Any]			)      ->		Optional[Any]:





         '''simple docstring'''
         A__					:       Optional[int]     =TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)
         A__					:       str     =XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""			)

         A__					:       Optional[int]     ="""left"""

         # use different length sentences to test batching
         A__					:       Tuple     =[
             """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__					:       Dict     =tokenizer(lowerCAmelCase_		,      return_tensors="""tf"""		,      padding=lowerCAmelCase_			)
         A__					:       Optional[int]     =inputs["""input_ids"""]

         A__					:       Union[str, Any]     =model.generate(input_ids=lowerCAmelCase_		,      attention_mask=inputs["""attention_mask"""]		,      max_new_tokens=12			)

         A__					:       str     =tokenizer(sentences[0]		,      return_tensors="""tf"""			).input_ids
         A__					:       str     =model.generate(input_ids=lowerCAmelCase_		,      max_new_tokens=12			)

         A__					:       Any     =tokenizer(sentences[1]		,      return_tensors="""tf"""			).input_ids
         A__					:       Optional[int]     =model.generate(input_ids=lowerCAmelCase_		,      max_new_tokens=12			)

         A__					:       str     =tokenizer.batch_decode(lowerCAmelCase_		,      skip_special_tokens=lowerCAmelCase_			)
         A__					:       Tuple     =tokenizer.decode(output_non_padded[0]		,      skip_special_tokens=lowerCAmelCase_			)
         A__					:       int     =tokenizer.decode(output_padded[0]		,      skip_special_tokens=lowerCAmelCase_			)

         A__					:       Any     =[
             """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'''



from __future__ import annotations
def 						__lowerCamelCase	(       __snake_case					:		list[int]	)	->							bool:






						"""simple docstring"""





						return len(set(__snake_case	)	) == len(__snake_case	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

