


'''simple docstring'''



import math

from numpy import inf
from scipy.integrate import quad



def      UpperCAmelCase__   (      UpperCAmelCase_       :							float				)	->						float:
     if num <= 0:
          raise ValueError('math domain error'				)

     return quad(UpperCAmelCase_ ,       0 ,       UpperCAmelCase_ ,       args=(UpperCAmelCase_)				)[0]



def      UpperCAmelCase__   (      UpperCAmelCase_       :							float ,       UpperCAmelCase_       :							float				)	->						float:
     return math.pow(UpperCAmelCase_ ,       z - 1				) * math.exp(-x				)


if __name__ == "__main__":
      from doctest import testmod

      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   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   Dict					=     XGLMConfig
    lowerCamelCase    :   List[str]					=     {}
    lowerCamelCase    :   Union[str, Any]					=     '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_=5_12     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	)							->	Any:
         __lowerCamelCase  :      int														=						parent
         __lowerCamelCase  :      Optional[int]														=						batch_size
         __lowerCamelCase  :      Optional[Any]														=						seq_length
         __lowerCamelCase  :      Optional[int]														=						is_training
         __lowerCamelCase  :      str														=						use_input_mask
         __lowerCamelCase  :      Dict														=						use_labels
         __lowerCamelCase  :      Union[str, Any]														=						vocab_size
         __lowerCamelCase  :      List[Any]														=						d_model
         __lowerCamelCase  :      List[Any]														=						num_hidden_layers
         __lowerCamelCase  :      List[Any]														=						num_attention_heads
         __lowerCamelCase  :      Optional[Any]														=						ffn_dim
         __lowerCamelCase  :      List[Any]														=						activation_function
         __lowerCamelCase  :      List[Any]														=						activation_dropout
         __lowerCamelCase  :      List[Any]														=						attention_dropout
         __lowerCamelCase  :      Union[str, Any]														=						max_position_embeddings
         __lowerCamelCase  :      Tuple														=						initializer_range
         __lowerCamelCase  :      int														=						None
         __lowerCamelCase  :      int														=						0
         __lowerCamelCase  :      Tuple														=						2
         __lowerCamelCase  :      Tuple														=						1







    def  lowercase_							(				self      )							->	Any:
         return XGLMConfig.from_pretrained('facebook/xglm-564M'      )







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      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      )

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

         __lowerCamelCase  :      str														=						self.get_config()

         __lowerCamelCase  :      List[Any]														=						floats_tensor([self.num_hidden_layers, self.num_attention_heads]     ,	2      )

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )







    def  lowercase_							(				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  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      List[Any]														=						self.prepare_config_and_inputs()

         (
             (
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			
         )  :      str														=						config_and_inputs

         __lowerCamelCase  :      Union[str, Any]														=						{
             'input_ids': input_ids,
             'head_mask': head_mask,
         }

         return config, inputs_dict







@require_tf
class   UpperCAmelCase_    (_UpperCAmelCase   ,						_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Optional[Any]					=     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    lowerCamelCase    :   List[Any]					=     (TFXGLMForCausalLM,) if is_tf_available() else ()
    lowerCamelCase    :   Any					=     (
        {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
    )
    lowerCamelCase    :   List[Any]					=     False
    lowerCamelCase    :   Dict					=     False
    lowerCamelCase    :   Union[str, Any]					=     False







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      str														=						TFXGLMModelTester(self      )
         __lowerCamelCase  :      List[Any]														=						ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	n_embd=37      )







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







    @slow
    def  lowercase_							(				self      )							->	Optional[int]:
         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowerCamelCase  :      Optional[Any]														=						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  lowercase_							(				self      )							->	Any:
         super().test_resize_token_embeddings()







@require_tf
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    @slow
    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=True      )							->	List[str]:
         __lowerCamelCase  :      Optional[Any]														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      int														=						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  :      Optional[int]														=						[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  :      Any														=						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  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Any														=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      Tuple														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )

         tf.random.set_seed(0      )
         __lowerCamelCase  :      List[Any]														=						tokenizer('Today is a nice day and'     ,	return_tensors='tf'      )
         __lowerCamelCase  :      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'      ):
              __lowerCamelCase  :      Tuple														=						model.generate(SCREAMING_SNAKE_CASE_     ,	do_sample=SCREAMING_SNAKE_CASE_     ,	seed=[7, 0]      )
         __lowerCamelCase  :      Optional[Any]														=						tokenizer.decode(output_ids[0]     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Union[str, 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  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Tuple														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      Any														=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )

         __lowerCamelCase  :      Any														=						'left'

         # use different length sentences to test batching
         __lowerCamelCase  :      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  :      Any														=						tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors='tf'     ,	padding=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[int]														=						inputs['input_ids']

         __lowerCamelCase  :      str														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	attention_mask=inputs['attention_mask']     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Optional[int]														=						tokenizer(sentences[0]     ,	return_tensors='tf'      ).input_ids
         __lowerCamelCase  :      int														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Optional[Any]														=						tokenizer(sentences[1]     ,	return_tensors='tf'      ).input_ids
         __lowerCamelCase  :      Optional[Any]														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Union[str, Any]														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, 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  :      Optional[int]														=						[
             '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]      )






