"""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							:			Any							=		XGLMConfig
     _UpperCamelCase							:			List[Any]							=		{}
     _UpperCamelCase							:			Optional[int]							=		"gelu"






     def __init__(		self				,     a__				,     a__=14				,     a__=7				,     a__=True				,     a__=True				,     a__=True				,     a__=99				,     a__=32				,     a__=2				,     a__=4				,     a__=37				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=512				,     a__=0.0_2				,     ):
       _lowerCAmelCase						:       Optional[int]  			=     parent
       _lowerCAmelCase						:       int  			=     batch_size
       _lowerCAmelCase						:       Optional[Any]  			=     seq_length
       _lowerCAmelCase						:       Any  			=     is_training
       _lowerCAmelCase						:       Optional[int]  			=     use_input_mask
       _lowerCAmelCase						:       str  			=     use_labels
       _lowerCAmelCase						:       Any  			=     vocab_size
       _lowerCAmelCase						:       Optional[int]  			=     d_model
       _lowerCAmelCase						:       int  			=     num_hidden_layers
       _lowerCAmelCase						:       Union[str, Any]  			=     num_attention_heads
       _lowerCAmelCase						:       Union[str, Any]  			=     ffn_dim
       _lowerCAmelCase						:       Any  			=     activation_function
       _lowerCAmelCase						:       Tuple  			=     activation_dropout
       _lowerCAmelCase						:       int  			=     attention_dropout
       _lowerCAmelCase						:       Optional[int]  			=     max_position_embeddings
       _lowerCAmelCase						:       str  			=     initializer_range
       _lowerCAmelCase						:       List[Any]  			=     None
       _lowerCAmelCase						:       Tuple  			=     0
       _lowerCAmelCase						:       str  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     1






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     tf.clip_by_value(
           ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )				,     clip_value_min=0				,     clip_value_max=3  )

       _lowerCAmelCase						:       Tuple  			=     None
       if self.use_input_mask:
         _lowerCAmelCase						:       Dict  			=     random_attention_mask([self.batch_size, self.seq_length]  )

       _lowerCAmelCase						:       int  			=     self.get_config()

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

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
       )






     def     __A  (		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=a__				,     bos_token_id=self.bos_token_id				,     eos_token_id=self.eos_token_id				,     pad_token_id=self.pad_token_id				,     return_dict=a__				,     )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.prepare_config_and_inputs()

       (
           (
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	
       )						:       List[str]  			=     config_and_inputs

       _lowerCAmelCase						:       Tuple  			=     {
           """input_ids""": input_ids,
           """head_mask""": head_mask,
       }

       return config, inputs_dict







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






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     TFXGLMModelTester(self  )
       _lowerCAmelCase						:       Optional[Any]  			=     ConfigTester(self				,     config_class=a__				,     n_embd=37  )






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






     @slow
     def     __A  (		self  ):
       for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       Any  			=     TFXGLMModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )






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







@require_tf
class 						__A							( unittest.TestCase		):






     @slow
     def     __A  (		self				,     a__=True  ):
       _lowerCAmelCase						:       List[str]  			=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )
       _lowerCAmelCase						:       Tuple  			=     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						:       Any  			=     [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
       # fmt: on
       _lowerCAmelCase						:       str  			=     model.generate(a__				,     do_sample=a__				,     num_beams=1  )
       if verify_outputs:
         self.assertListEqual(output_ids[0].numpy().tolist()				,     a__  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     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						:       Optional[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						:       List[str]  			=     model.generate(a__				,     do_sample=a__				,     seed=[7, 0]  )
       _lowerCAmelCase						:       int  			=     tokenizer.decode(output_ids[0]				,     skip_special_tokens=a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     (
           """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
       )
       self.assertEqual(a__				,     a__  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )
       _lowerCAmelCase						:       List[Any]  			=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )

       _lowerCAmelCase						:       Optional[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						:       int  			=     tokenizer(a__				,     return_tensors="""tf"""				,     padding=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     inputs["""input_ids"""]

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

       _lowerCAmelCase						:       Any  			=     tokenizer(sentences[0]				,     return_tensors="""tf"""  ).input_ids
       _lowerCAmelCase						:       List[Any]  			=     model.generate(input_ids=a__				,     max_new_tokens=12  )

       _lowerCAmelCase						:       str  			=     tokenizer(sentences[1]				,     return_tensors="""tf"""  ).input_ids
       _lowerCAmelCase						:       Optional[Any]  			=     model.generate(input_ids=a__				,     max_new_tokens=12  )

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

       _lowerCAmelCase						:       Dict  			=     [
           """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(a__				,     a__  )
       self.assertListEqual(a__				,     [non_padded_sentence, padded_sentence]  )





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:  # noqa: E741
  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     0
  _lowerCAmelCase						:       Any  			=     [0] * n
  _lowerCAmelCase						:       str  			=     [False] * n
  _lowerCAmelCase						:       str  			=     [False] * n

  def dfs(_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	):
    if parent == root:
      out_edge_count += 1
    _lowerCAmelCase						:       Any  			=     True
    _lowerCAmelCase						:       int  			=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _lowerCAmelCase						:       Union[str, Any]  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        _lowerCAmelCase						:       Optional[int]  			=     min(low[at]			,low[to]	)

        # AP found via bridge
        if at < low[to]:
          _lowerCAmelCase						:       int  			=     True
        # AP found via cycle
        if at == low[to]:
          _lowerCAmelCase						:       Tuple  			=     True
      else:
        _lowerCAmelCase						:       Union[str, Any]  			=     min(low[at]			,_lowerCamelCase	)
    return out_edge_count

  for i in range(_lowerCamelCase	):
    if not visited[i]:
      _lowerCAmelCase						:       int  			=     0
      _lowerCAmelCase						:       Dict  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,-1			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     out_edge_count > 1

  for x in range(len(_lowerCamelCase	)	):
    if is_art[x] is True:
      print(_lowerCamelCase	)


# Adjacency list of graph
_a      :		Optional[Any]           =   {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)




