

"""simple docstring"""




_lowercase   :	Union[str, Any]												=						{
    0: '0',
    1: '1',
    2: '2',
    3: '3',
    4: '4',
    5: '5',
    6: '6',
    7: '7',
    8: '8',
    9: '9',
    10: 'a',
    11: 'b',
    12: 'c',
    13: 'd',
    14: 'e',
    15: 'f',
}





def       lowercase__						(       snake_case_   :float    ):
	assert type(snake_case_    ) in (int, float) and decimal == int(snake_case_    )
	__UpperCAmelCase      		= int(snake_case_    )
	__UpperCAmelCase      		= ''''''
	__UpperCAmelCase      		= False
	if decimal < 0:
		__UpperCAmelCase      		= True
		decimal *= -1
	while decimal > 0:
		__UpperCAmelCase		,							__UpperCAmelCase      		= divmod(snake_case_			,		16    )
		__UpperCAmelCase      		= values[remainder] + hexadecimal
	__UpperCAmelCase      		= '''0x''' + hexadecimal
	if negative:
		__UpperCAmelCase      		= '''-''' + hexadecimal
	return hexadecimal


if __name__ == "__main__":
				import doctest

				doctest.testmod()




"""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 	lowercase__				:
   __UpperCAmelCase							       =					XGLMConfig
   __UpperCAmelCase							       =					{}
   __UpperCAmelCase							       =					'''gelu'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=14      ,   SCREAMING_SNAKE_CASE=7      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=99      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=37      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=0.02      ,   )  ->  List[str]:
      _lowerCamelCase	:					Optional[int]   =		parent
      _lowerCamelCase	:					int   =		batch_size
      _lowerCamelCase	:					str   =		seq_length
      _lowerCamelCase	:					Any   =		is_training
      _lowerCamelCase	:					int   =		use_input_mask
      _lowerCamelCase	:					Union[str, Any]   =		use_labels
      _lowerCamelCase	:					str   =		vocab_size
      _lowerCamelCase	:					List[str]   =		d_model
      _lowerCamelCase	:					List[Any]   =		num_hidden_layers
      _lowerCamelCase	:					Dict   =		num_attention_heads
      _lowerCamelCase	:					int   =		ffn_dim
      _lowerCamelCase	:					str   =		activation_function
      _lowerCamelCase	:					Optional[int]   =		activation_dropout
      _lowerCamelCase	:					Tuple   =		attention_dropout
      _lowerCamelCase	:					Tuple   =		max_position_embeddings
      _lowerCamelCase	:					Dict   =		initializer_range
      _lowerCamelCase	:					Optional[Any]   =		None
      _lowerCamelCase	:					Union[str, Any]   =		0
      _lowerCamelCase	:					List[Any]   =		2
      _lowerCamelCase	:					str   =		1





   def 		UpperCamelCase_  (							self)  ->  int:
      return XGLMConfig.from_pretrained("""facebook/xglm-564M""")





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Union[str, Any]   =		tf.clip_by_value(
          ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)      ,   clip_value_min=0      ,   clip_value_max=3)

      _lowerCamelCase	:					str   =		None
      if self.use_input_mask:
         _lowerCamelCase	:					Union[str, Any]   =		random_attention_mask([self.batch_size, self.seq_length])

      _lowerCamelCase	:					Tuple   =		self.get_config()

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

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
      )





   def 		UpperCamelCase_  (							self)  ->  Optional[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=SCREAMING_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=SCREAMING_SNAKE_CASE      ,   )





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					List[Any]   =		self.prepare_config_and_inputs()

      (
          (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    
      )	:					str   =		config_and_inputs

      _lowerCamelCase	:					Optional[Any]   =		{
          """input_ids""": input_ids,
          """head_mask""": head_mask,
      }

      return config, inputs_dict







@require_tf
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
   __UpperCAmelCase							       =					(TFXGLMForCausalLM,) if is_tf_available() else ()
   __UpperCAmelCase							       =					(
       {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
   )
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Optional[Any]   =		TFXGLMModelTester(self)
      _lowerCamelCase	:					str   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   n_embd=37)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      self.config_tester.run_common_tests()





   @slow
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCamelCase	:					Tuple   =		TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)





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







@require_tf
class 	lowercase__				(					unittest.TestCase				):





   @slow
   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE=True)  ->  List[Any]:
      _lowerCamelCase	:					List[str]   =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
      _lowerCamelCase	:					Union[str, Any]   =		tf.convert_to_tensor([[2, 268, 9865]]      ,   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	:					Dict   =		[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
      # fmt: on
      _lowerCamelCase	:					str   =		model.generate(SCREAMING_SNAKE_CASE      ,   do_sample=SCREAMING_SNAKE_CASE      ,   num_beams=1)
      if verify_outputs:
         self.assertListEqual(output_ids[0].numpy().tolist()      ,   SCREAMING_SNAKE_CASE)





   @slow
   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					int   =		XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")
      _lowerCamelCase	:					Tuple   =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")

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

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





   @slow
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Optional[Any]   =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
      _lowerCamelCase	:					Any   =		XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")

      _lowerCamelCase	:					List[Any]   =		"""left"""

      # use different length sentences to test batching
      _lowerCamelCase	:					List[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""",
          """Hello, my dog is a little""",
      ]

      _lowerCamelCase	:					Union[str, Any]   =		tokenizer(SCREAMING_SNAKE_CASE      ,   return_tensors="""tf"""      ,   padding=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		inputs["""input_ids"""]

      _lowerCamelCase	:					List[Any]   =		model.generate(input_ids=SCREAMING_SNAKE_CASE      ,   attention_mask=inputs["""attention_mask"""]      ,   max_new_tokens=12)

      _lowerCamelCase	:					List[str]   =		tokenizer(sentences[0]      ,   return_tensors="""tf""").input_ids
      _lowerCamelCase	:					Optional[Any]   =		model.generate(input_ids=SCREAMING_SNAKE_CASE      ,   max_new_tokens=12)

      _lowerCamelCase	:					Tuple   =		tokenizer(sentences[1]      ,   return_tensors="""tf""").input_ids
      _lowerCamelCase	:					int   =		model.generate(input_ids=SCREAMING_SNAKE_CASE      ,   max_new_tokens=12)

      _lowerCamelCase	:					Optional[int]   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		tokenizer.decode(output_non_padded[0]      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		tokenizer.decode(output_padded[0]      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					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 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(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
      self.assertListEqual(SCREAMING_SNAKE_CASE      ,   [non_padded_sentence, padded_sentence])

