



'''simple docstring'''




import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow







class 	_lowerCAmelCase							:


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				return None







class 	_lowerCAmelCase							:


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase							):
				return None







class 	_lowerCAmelCase							(			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					[
	    # (model_name, model_kwargs)
	    ('bert-base-cased', {}),
	    ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
	]


	@require_tf
	@slow
	def     _a	(self							):
				for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							self._test_export(lowercase       ,      """tf"""       ,      12       ,      **lowercase							)


	@require_torch
	@slow
	def     _a	(self							):
				for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							self._test_export(lowercase       ,      """pt"""       ,      12       ,      **lowercase							)


	@require_torch
	@slow
	def     _a	(self							):
				from transformers import BertModel

				A_	:						int	=     ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
				with NamedTemporaryFile(mode="""w+t"""							) as vocab_file:
							vocab_file.write("""\n""".join(lowercase							)							)
							vocab_file.flush()
							A_	:						List[Any]	=     BertTokenizerFast(vocab_file.name							)

				with TemporaryDirectory() as bert_save_dir:
							A_	:						Dict	=     BertModel(BertConfig(vocab_size=len(lowercase							)							)							)
							model.save_pretrained(lowercase							)
							self._test_export(lowercase       ,      """pt"""       ,      12       ,      lowercase							)


	@require_tf
	@slow
	def     _a	(self							):
				for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							A_	:						int	=     self._test_export(lowercase       ,      """tf"""       ,      12       ,      **lowercase							)
							A_	:						Any	=     quantize(Path(lowercase							)							)

							# Ensure the actual quantized model is not bigger than the original one
							if quantized_path.stat().st_size >= Path(lowercase							).stat().st_size:
										self.fail("""Quantized model is bigger than initial ONNX model"""							)


	@require_torch
	@slow
	def     _a	(self							):
				for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
							A_	:						Optional[int]	=     self._test_export(lowercase       ,      """pt"""       ,      12       ,      **lowercase							)
							A_	:						Dict	=     quantize(lowercase							)

							# Ensure the actual quantized model is not bigger than the original one
							if quantized_path.stat().st_size >= Path(lowercase							).stat().st_size:
										self.fail("""Quantized model is bigger than initial ONNX model"""							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase=None       ,      **lowercase							):
				try:
							# Compute path
							with TemporaryDirectory() as tempdir:
										A_	:						Any	=     Path(lowercase							).joinpath("""model.onnx"""							)

							# Remove folder if exists
							if path.parent.exists():
										path.parent.rmdir()

							# Export
							convert(lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      **lowercase							)

							return path
				except Exception as e:
							self.fail(lowercase							)


	@require_torch
	@require_tokenizers
	@slow
	def     _a	(self							):
				from transformers import BertModel

				A_	:						Optional[int]	=     BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""							)							)
				A_	:						List[Any]	=     BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""							)
				self._test_infer_dynamic_axis(lowercase       ,      lowercase       ,      """pt"""							)


	@require_tf
	@require_tokenizers
	@slow
	def     _a	(self							):
				from transformers import TFBertModel

				A_	:						List[str]	=     TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""							)							)
				A_	:						Any	=     BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""							)
				self._test_infer_dynamic_axis(lowercase       ,      lowercase       ,      """tf"""							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						List[str]	=     FeatureExtractionPipeline(lowercase       ,      lowercase							)

				A_	:						int	=     ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
				A_,    A_,    A_,    A_	:						Any	=     infer_shapes(lowercase       ,      lowercase							)

				# Assert all variables are present
				self.assertEqual(len(lowercase							)       ,      len(lowercase							)							)
				self.assertTrue(all(var_name in shapes for var_name in variable_names							)							)
				self.assertSequenceEqual(variable_names[:3]       ,      lowercase							)
				self.assertSequenceEqual(variable_names[3:]       ,      lowercase							)

				# Assert inputs are {0: batch, 1: sequence}
				for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
							self.assertDictEqual(shapes[var_name]       ,      {0: """batch""", 1: """sequence"""}							)

				# Assert outputs are {0: batch, 1: sequence} and {0: batch}
				self.assertDictEqual(shapes["""output_0"""]       ,      {0: """batch""", 1: """sequence"""}							)
				self.assertDictEqual(shapes["""output_1"""]       ,      {0: """batch"""}							)


	def     _a	(self							):
				A_	:						Dict	=     ["""input_ids""", """attention_mask""", """token_type_ids"""]
				A_	:						Optional[int]	=     {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
				A_,    A_	:						List[str]	=     ensure_valid_input(FuncContiguousArgs()       ,      lowercase       ,      lowercase							)

				# Should have exactly the same number of args (all are valid)
				self.assertEqual(len(lowercase							)       ,      3							)

				# Should have exactly the same input names
				self.assertEqual(set(lowercase							)       ,      set(lowercase							)							)

				# Parameter should be reordered according to their respective place in the function:
				# (input_ids, token_type_ids, attention_mask)
				self.assertEqual(lowercase       ,      (tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])							)

				# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
				A_,    A_	:						Optional[int]	=     ensure_valid_input(FuncNonContiguousArgs()       ,      lowercase       ,      lowercase							)

				# Should have exactly the one arg (all before the one not provided "some_other_args")
				self.assertEqual(len(lowercase							)       ,      1							)
				self.assertEqual(len(lowercase							)       ,      1							)

				# Should have only "input_ids"
				self.assertEqual(inputs_args[0]       ,      tokens["""input_ids"""]							)
				self.assertEqual(ordered_input_names[0]       ,      """input_ids"""							)


	def     _a	(self							):
				A_	:						Any	=     generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""							)       ,      """-test"""							)
				self.assertEqual("""/home/something/my_fake_model-test.onnx"""       ,      generated.as_posix()							)




'''simple docstring'''




class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[Any]	=     [
				    [],
				    [],
				    [],
				]


	def     _a	(self       ,      lowercase       ,      lowercase							):
				try:
							if len(self.queues[priority]							) >= 100:
										raise OverflowError("""Maximum queue size is 100"""							)
							self.queues[priority].append(lowercase							)
				except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""							)


	def     _a	(self							):
				for queue in self.queues:
							if queue:
										return queue.pop(0							)
				raise UnderFlowError("""All queues are empty"""							)


	def __str__(self							):
				return "\n".join(F'Priority {i}: {q}' for i, q in enumerate(self.queues							)							)







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[str]	=     []


	def     _a	(self       ,      lowercase							):
				if len(self.queue							) == 100:
							raise OverFlowError("""Maximum queue size is 100"""							)
				self.queue.append(lowercase							)


	def     _a	(self							):
				if not self.queue:
							raise UnderFlowError("""The queue is empty"""							)
				else:
							A_	:						List[Any]	=     min(self.queue							)
							self.queue.remove(lowercase							)
							return data


	def __str__(self							):
				return str(self.queue							)






def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     FixedPriorityQueue()
			fpq.enqueue(0						,      10    )
			fpq.enqueue(1						,      70    )
			fpq.enqueue(0						,      1_00    )
			fpq.enqueue(2						,      1    )
			fpq.enqueue(2						,      5    )
			fpq.enqueue(1						,      7    )
			fpq.enqueue(2						,      4    )
			fpq.enqueue(1						,      64    )
			fpq.enqueue(0						,      1_28    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )



def 	a			( ):
			'''simple docstring'''





			A_	:						int	=     ElementPriorityQueue()
			epq.enqueue(10    )
			epq.enqueue(70    )
			epq.enqueue(1_00    )
			epq.enqueue(1    )
			epq.enqueue(5    )
			epq.enqueue(7    )
			epq.enqueue(4    )
			epq.enqueue(64    )
			epq.enqueue(1_28    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )


if __name__ == "__main__":
						fixed_priority_queue()
						element_priority_queue()