





'''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 __a :
   __UpperCamelCase   : Optional[Any]	      =	XGLMConfig
   __UpperCamelCase   : Optional[Any]	      =	{}
   __UpperCamelCase   : Any	      =	'gelu'





   def __init__(					self				:			Optional[Any]				,lowerCamelCase				:			List[str]				,lowerCamelCase				:			List[str]=14				,lowerCamelCase				:			int=7				,lowerCamelCase				:			Tuple=True				,lowerCamelCase				:			Optional[int]=True				,lowerCamelCase				:			Union[str, Any]=True				,lowerCamelCase				:			Tuple=99				,lowerCamelCase				:			List[Any]=32				,lowerCamelCase				:			List[str]=2				,lowerCamelCase				:			Union[str, Any]=4				,lowerCamelCase				:			List[str]=37				,lowerCamelCase				:			List[str]="gelu"				,lowerCamelCase				:			List[str]=0.1				,lowerCamelCase				:			List[str]=0.1				,lowerCamelCase				:			Optional[Any]=512				,lowerCamelCase				:			int=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      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''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      UpperCAmelCase__     (					self				:			Dict    ):

      '''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      UpperCAmelCase__     (					self				:			Any    ):

      '''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 __a (   _snake_case,   _snake_case,   unittest.TestCase			):
   __UpperCamelCase   : str	      =	(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
   __UpperCamelCase   : Optional[int]	      =	(TFXGLMForCausalLM,) if is_tf_available() else ()
   __UpperCamelCase   : Tuple	      =	(
       {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
   )
   __UpperCamelCase   : Optional[Any]	      =	False
   __UpperCamelCase   : Tuple	      =	False
   __UpperCamelCase   : List[str]	      =	False





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       TFXGLMModelTester(self    )
      __SCREAMING_SNAKE_CASE      							=       ConfigTester(self				,config_class=lowerCamelCase				,n_embd=37    )





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   @slow
   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

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





   @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""    )
   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      super().test_resize_token_embeddings()







@require_tf
class __a (   unittest.TestCase			):





   @slow
   def      UpperCAmelCase__     (					self				:			str				,lowerCamelCase				:			str=True    ):

      '''simple docstring'''

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





   @slow
   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''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(lowerCamelCase				,do_sample=lowerCamelCase				,seed=[7, 0]    )
      __SCREAMING_SNAKE_CASE      							=       tokenizer.decode(output_ids[0]				,skip_special_tokens=lowerCamelCase    )

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





   @slow
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''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(lowerCamelCase				,return_tensors="""tf"""				,padding=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       inputs["""input_ids"""]

      __SCREAMING_SNAKE_CASE      							=       model.generate(input_ids=lowerCamelCase				,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=lowerCamelCase				,max_new_tokens=12    )

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

      __SCREAMING_SNAKE_CASE      							=       tokenizer.batch_decode(lowerCamelCase				,skip_special_tokens=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       tokenizer.decode(output_non_padded[0]				,skip_special_tokens=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       tokenizer.decode(output_padded[0]				,skip_special_tokens=lowerCamelCase    )

      __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(lowerCamelCase				,lowerCamelCase    )
      self.assertListEqual(lowerCamelCase				,[non_padded_sentence, padded_sentence]    )












'''simple docstring'''

def 						__magic_name__       (							__UpperCAmelCase = 1							,	__UpperCAmelCase = 1000      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       1
   __SCREAMING_SNAKE_CASE      							=       0

   for divide_by_number in range(__UpperCAmelCase							,	digit + 1      ):
      __SCREAMING_SNAKE_CASE      							=       []
      __SCREAMING_SNAKE_CASE      							=       numerator
      for _ in range(1							,	digit + 1      ):
         if now_divide in has_been_divided:
            if longest_list_length < len(__UpperCAmelCase      ):
               __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
               __SCREAMING_SNAKE_CASE      							=       divide_by_number
         else:
            has_been_divided.append(__UpperCAmelCase      )
            __SCREAMING_SNAKE_CASE      							=       now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





