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 __snake_case       :
   lowerCAmelCase_														=							XGLMConfig
   lowerCAmelCase_														=							{}
   lowerCAmelCase_														=							"gelu"




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



     """simple docstring"""






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




   def 			__a		(					self    :       str   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   tf.clip_by_value(
         ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size   ) ,			clip_value_min=0 ,			clip_value_max=3   )

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

     SCREAMING_SNAKE_CASE__    							=   self.get_config()

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

     return (
         config,
         input_ids,
         input_mask,
         head_mask,
     )




   def 			__a		(					self    :       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 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.prepare_config_and_inputs()

     (
         (
         SCREAMING_SNAKE_CASE__					
     )							,  (
         SCREAMING_SNAKE_CASE__					
     )							,  (
         SCREAMING_SNAKE_CASE__					
     )							,  (
         SCREAMING_SNAKE_CASE__					
     )							,  					
     )    							=   config_and_inputs

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

     return config, inputs_dict







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




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   @slow
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






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




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



     """simple docstring"""






     super().test_resize_token_embeddings()







@require_tf
class __snake_case       (		unittest.TestCase	):




   @slow
   def 			__a		(					self    :       str ,			_lowercase    :       List[Any]=True   ):



     """simple docstring"""






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




   @slow
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""   )
     SCREAMING_SNAKE_CASE__    							=   TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""   )

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

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




   @slow
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""   )
     SCREAMING_SNAKE_CASE__    							=   XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""   )

     SCREAMING_SNAKE_CASE__    							=   """left"""

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

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

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

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

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

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

     SCREAMING_SNAKE_CASE__    							=   [
         """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 __future__ import annotations



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list			)							->      float:







  """simple docstring"""





  if not nums:
    raise ValueError("""List is empty"""			)
  return sum(__UpperCamelCase			) / len(__UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
