


'''simple docstring'''


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_tensorflow_text_available():
						from transformers.models.bert import TFBertTokenizer


lowerCamelCase__    	=     ['bert-base-uncased', 'bert-base-cased']
lowerCamelCase__    	=     'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():






						class 	lowerCAmelCase__			(			tf.keras.Model	):




									def __init__(		self  :     Dict	,  lowerCamelCase__  :     str )   ->List[Any]:



											'''simple docstring'''







											super().__init__()
											_UpperCAmelCase		:       Tuple								=      tokenizer
											_UpperCAmelCase		:       str								=      AutoConfig.from_pretrained(lowerCamelCase__ )
											_UpperCAmelCase		:       Optional[Any]								=      TFAutoModel.from_config(lowerCamelCase__ )






									def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     int )   ->int:



											'''simple docstring'''







											_UpperCAmelCase		:       List[str]								=      self.tokenizer(lowerCamelCase__ )
											_UpperCAmelCase		:       List[Any]								=      self.bert(**lowerCamelCase__ )
											return out["pooler_output"]






@require_tf
@require_tensorflow_text
class 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Union[str, Any]:



					'''simple docstring'''







					super().setUp()

					_UpperCAmelCase		:       Dict								=      [
					    BertTokenizer.from_pretrained(lowerCamelCase__ ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
					]  # repeat for when fast_bert_tokenizer=false
					_UpperCAmelCase		:       int								=      [TFBertTokenizer.from_pretrained(lowerCamelCase__ ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
					    TFBertTokenizer.from_pretrained(lowerCamelCase__	,  use_fast_bert_tokenizer=lowerCamelCase__ )
					    for checkpoint in TOKENIZER_CHECKPOINTS
					]
					assert len(self.tokenizers ) == len(self.tf_tokenizers )

					_UpperCAmelCase		:       List[str]								=      [
					    "This is a straightforward English test sentence.",
					    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
					    "Now we're going to add some Chinese: 一 二 三 一二三",
					    "And some much more rare Chinese: 齉 堃 齉堃",
					    "Je vais aussi écrire en français pour tester les accents",
					    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
					]
					_UpperCAmelCase		:       Any								=      list(zip(self.test_sentences	,  self.test_sentences[::-1] ) )




			def 							lowerCAmelCase__    (		self  :     Any )   ->List[str]:



					'''simple docstring'''







					for tokenizer, tf_tokenizer in zip(self.tokenizers	,  self.tf_tokenizers ):
							for test_inputs in (self.test_sentences, self.paired_sentences):
									_UpperCAmelCase		:       Any								=      tokenizer(lowerCamelCase__	,  return_tensors="tf"	,  padding="longest" )
									_UpperCAmelCase		:       Optional[int]								=      tf_tokenizer(lowerCamelCase__ )

									for key in python_outputs.keys():
											self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
											self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,  tf.intaa ) == tf_outputs[key] ) )




			@slow
			def 							lowerCAmelCase__    (		self  :     Dict )   ->int:



					'''simple docstring'''







					for tf_tokenizer in self.tf_tokenizers:
							_UpperCAmelCase		:       int								=      tf_tokenizer(self.paired_sentences )
							_UpperCAmelCase		:       Dict								=      tf_tokenizer(
							    text=[sentence[0] for sentence in self.paired_sentences]	,  text_pair=[sentence[1] for sentence in self.paired_sentences]	,  )
							for key in merged_outputs.keys():
									self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,  tf.intaa ) == separated_outputs[key] ) )




			@slow
			def 							lowerCAmelCase__    (		self  :     Dict )   ->str:



					'''simple docstring'''







					for tf_tokenizer in self.tf_tokenizers:
							_UpperCAmelCase		:       Optional[Any]								=      tf.function(lowerCamelCase__ )
							for test_inputs in (self.test_sentences, self.paired_sentences):
									_UpperCAmelCase		:       Union[str, Any]								=      tf.constant(lowerCamelCase__ )
									_UpperCAmelCase		:       List[Any]								=      compiled_tokenizer(lowerCamelCase__ )
									_UpperCAmelCase		:       str								=      tf_tokenizer(lowerCamelCase__ )

									for key in eager_outputs.keys():
											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )






			@slow
			def 							lowerCAmelCase__    (		self  :     Any )   ->Any:



					'''simple docstring'''







					for tf_tokenizer in self.tf_tokenizers:
							_UpperCAmelCase		:       Any								=      ModelToSave(tokenizer=lowerCamelCase__ )
							_UpperCAmelCase		:       List[Any]								=      tf.convert_to_tensor(self.test_sentences )
							_UpperCAmelCase		:       Any								=      model(lowerCamelCase__ )  # Build model with some sample inputs
							with TemporaryDirectory() as tempdir:
									_UpperCAmelCase		:       List[Any]								=      Path(lowerCamelCase__ ) / "saved.model"
									model.save(lowerCamelCase__ )
									_UpperCAmelCase		:       str								=      tf.keras.models.load_model(lowerCamelCase__ )
							_UpperCAmelCase		:       Union[str, Any]								=      loaded_model(lowerCamelCase__ )
							# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
							self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) )	,  1E-5 )







'''simple docstring'''


from __future__ import annotations

lowerCamelCase__    	=     {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     dict[str, list[str]]	,  lowerCamelCase__  :     str )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      graph
					# mapping node to its parent in resulting breadth first tree
					_UpperCAmelCase		:       dict[str, str | None]								=      {}
					_UpperCAmelCase		:       List[Any]								=      source_vertex




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      {self.source_vertex}
					_UpperCAmelCase		:       List[Any]								=      None
					_UpperCAmelCase		:       List[str]								=      [self.source_vertex]  # first in first out queue

					while queue:
							_UpperCAmelCase		:       int								=      queue.pop(0 )
							for adjacent_vertex in self.graph[vertex]:
									if adjacent_vertex not in visited:
											visited.add(lowerCamelCase__ )
											_UpperCAmelCase		:       List[Any]								=      vertex
											queue.append(lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->str:



					'''simple docstring'''







					if target_vertex == self.source_vertex:
							return self.source_vertex

					_UpperCAmelCase		:       int								=      self.parent.get(lowerCamelCase__ )
					if target_vertex_parent is None:
							_UpperCAmelCase		:       Tuple								=      (
							    F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
							)
							raise ValueError(lowerCamelCase__ )

					return self.shortest_path(lowerCamelCase__ ) + F"""->{target_vertex}"""


if __name__ == "__main__":
						lowerCamelCase__    	=     Graph(graph, 'G')
						g.breath_first_search()
						print(g.shortest_path('D'))
						print(g.shortest_path('G'))
						print(g.shortest_path('Foo'))



