'''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							:
       snake_case							:Optional[Any]					     =       XGLMConfig
       snake_case							:Tuple					     =       {}
       snake_case							:Any					     =       "gelu"






       def __init__(   self					,  UpperCamelCase_					,  UpperCamelCase_=14					,  UpperCamelCase_=7					,  UpperCamelCase_=True					,  UpperCamelCase_=True					,  UpperCamelCase_=True					,  UpperCamelCase_=99					,  UpperCamelCase_=32					,  UpperCamelCase_=2					,  UpperCamelCase_=4					,  UpperCamelCase_=37					,  UpperCamelCase_="gelu"					,  UpperCamelCase_=0.1					,  UpperCamelCase_=0.1					,  UpperCamelCase_=5_12					,  UpperCamelCase_=0.0_2					,  ):
              __UpperCAmelCase	:					Optional[int]												=		parent
              __UpperCAmelCase	:					int												=		batch_size
              __UpperCAmelCase	:					str												=		seq_length
              __UpperCAmelCase	:					Any												=		is_training
              __UpperCAmelCase	:					int												=		use_input_mask
              __UpperCAmelCase	:					Union[str, Any]												=		use_labels
              __UpperCAmelCase	:					str												=		vocab_size
              __UpperCAmelCase	:					List[str]												=		d_model
              __UpperCAmelCase	:					List[Any]												=		num_hidden_layers
              __UpperCAmelCase	:					Dict												=		num_attention_heads
              __UpperCAmelCase	:					int												=		ffn_dim
              __UpperCAmelCase	:					str												=		activation_function
              __UpperCAmelCase	:					Optional[int]												=		activation_dropout
              __UpperCAmelCase	:					Tuple												=		attention_dropout
              __UpperCAmelCase	:					Tuple												=		max_position_embeddings
              __UpperCAmelCase	:					Dict												=		initializer_range
              __UpperCAmelCase	:					Optional[Any]												=		None
              __UpperCAmelCase	:					Union[str, Any]												=		0
              __UpperCAmelCase	:					List[Any]												=		2
              __UpperCAmelCase	:					str												=		1






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






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					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   )

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

              __UpperCAmelCase	:					Tuple												=		self.get_config()

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

              return (
                  config,
                  input_ids,
                  input_mask,
                  head_mask,
              )






       def       _snake_case    (   self   ):
              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       _snake_case    (   self   ):
              __UpperCAmelCase	:					List[Any]												=		self.prepare_config_and_inputs()

              (
                  __UpperCAmelCase    
              )	:					str												=		config_and_inputs

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

              return config, inputs_dict






@require_tf
class 		__A							(A_			,       A_			,       unittest.TestCase ):
       snake_case							:Tuple					     =       (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
       snake_case							:str					     =       (TFXGLMForCausalLM,) if is_tf_available() else ()
       snake_case							:Dict					     =       (
           {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
       )
       snake_case							:Optional[Any]					     =       False
       snake_case							:int					     =       False
       snake_case							:Tuple					     =       False






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Optional[Any]												=		TFXGLMModelTester(self   )
              __UpperCAmelCase	:					str												=		ConfigTester(self					,  config_class=UpperCamelCase_					,  n_embd=37   )






       def       _snake_case    (   self   ):
              self.config_tester.run_common_tests()






       @slow
       def       _snake_case    (   self   ):
              for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     __UpperCAmelCase	:					Tuple												=		TFXGLMModel.from_pretrained(UpperCamelCase_   )
                     self.assertIsNotNone(UpperCamelCase_   )







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






@require_tf
class 		__A							(unittest.TestCase ):






       @slow
       def       _snake_case    (   self					,  UpperCamelCase_=True   ):
              __UpperCAmelCase	:					List[str]												=		TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"   )
              __UpperCAmelCase	:					Union[str, Any]												=		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
              __UpperCAmelCase	:					Dict												=		[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
              __UpperCAmelCase	:					str												=		model.generate(UpperCamelCase_					,  do_sample=UpperCamelCase_					,  num_beams=1   )
              if verify_outputs:
                     self.assertListEqual(output_ids[0].numpy().tolist()					,  UpperCamelCase_   )






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

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

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







       @slow
       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Optional[Any]												=		TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"   )
              __UpperCAmelCase	:					Any												=		XGLMTokenizer.from_pretrained("facebook/xglm-564M"   )

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

              # use different length sentences to test batching
              __UpperCAmelCase	:					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""",
              ]

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

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

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

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

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

              __UpperCAmelCase	:					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(UpperCamelCase_					,  UpperCamelCase_   )
              self.assertListEqual(UpperCamelCase_					,  [non_padded_sentence, padded_sentence]   )










"""simple docstring"""
def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int						, __snake_case							:      int						, __snake_case							:      list[int]   ):




   """simple docstring"""







   if graph[path[curr_ind - 1]][next_ver] == 0:
      return False

   # 2. Validate that next vertex is not already in path
   return not any(vertex == next_ver for vertex in path   )

def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      list[int]						, __snake_case							:      int   ):




   """simple docstring"""







   if curr_ind == len(__snake_case   ):
      # return whether path exists between current and starting vertices
      return graph[path[curr_ind - 1]][path[0]] == 1

   # Recursive Step
   for next_ver in range(0						, len(__snake_case   )   ):
      if valid_connection(__snake_case						, __snake_case						, __snake_case						, __snake_case   ):
         # Insert current vertex  into path as next transition
         _lowerCamelCase	:					List[str]   =		next_ver
         # Validate created path
         if util_hamilton_cycle(__snake_case						, __snake_case						, curr_ind + 1   ):
            return True
         # Backtrack
         _lowerCamelCase	:					Tuple   =		-1
   return False





def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int = 0   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		[-1] * (len(__snake_case   ) + 1)
   # initialize start and end of path with starting index
   _lowerCamelCase	:					Optional[int]   =		start_index
   # evaluate and if we find answer return path either return empty array
   return path if util_hamilton_cycle(__snake_case						, __snake_case						, 1   ) else []

