
import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]    ):
       monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"				,					set()    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : str    ):






       class   UpperCAmelCase		:



          def __init__(self		:					Optional[int]    ,						snake_case__		:					str )		->	Any:


                 '''simple docstring'''


                 snake_case       :   List[str]							=      metric_id






       class   UpperCAmelCase		:
          A__					:		List[str]						     =     [MetricMock(A_ ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]



          def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


                 '''simple docstring'''


                 return self._metrics



       monkeypatch.setattr("datasets.inspect.huggingface_hub"				,					HfhMock()    )




@pytest.mark.parametrize(
    "func, args"				,					[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]    )
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Any    ):
       if "tmp_path" in args:
              snake_case       :   str							=      tuple(arg if arg != "tmp_path" else tmp_path for arg in args    )
       with pytest.warns(__lowerCamelCase				,					match="https://huggingface.co/docs/evaluate"    ):
              func(*__lowerCamelCase    )









import os

import numpy
import onnx







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			a.name
		__lowercase=			b.name

		__lowercase=			''
		__lowercase=			''

		__lowercase=			a == b

		__lowercase=			name_a
		__lowercase=			name_b

		return res







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		for i, input_name in enumerate(node_proto.input	):
				if input_name == name:
						node_proto.input.insert(lowercase__   , lowercase__	)
						node_proto.input.pop(i + 1	)

		if node_proto.op_type == "If":
				_graph_replace_input_with(node_proto.attribute[0].g   , lowercase__   , lowercase__	)
				_graph_replace_input_with(node_proto.attribute[1].g   , lowercase__   , lowercase__	)
		if node_proto.op_type == "Loop":
				_graph_replace_input_with(node_proto.attribute[0].g   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	str:

		'''simple docstring'''







		for n in graph_proto.node:
				_node_replace_input_with(lowercase__   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		__lowercase=			list(model.graph.initializer	)
		__lowercase=			list(model_without_ext.graph.initializer	)
		for i, ref_i in ind_to_replace:
				assert inits_with_data[i].name == inits[i].name
				assert inits_with_data[ref_i].name == inits[ref_i].name
				assert i > ref_i

				__lowercase=			inits[i].name
				__lowercase=			inits[ref_i].name

				model_without_ext.graph.initializer.remove(inits[i]	)

				# for n in model.graph.node:
				_graph_replace_input_with(model_without_ext.graph   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__	)						->	Dict:

		'''simple docstring'''







		__lowercase=			os.path.dirname(lowercase__	)
		__lowercase=			os.path.basename(lowercase__	)

		__lowercase=			onnx.load(os.path.join(lowercase__   , lowercase__	)	)

		__lowercase=			list(model.graph.initializer	)

		__lowercase=			set()
		__lowercase=			{}
		__lowercase=			[]

		__lowercase=			0

		for i in range(len(lowercase__	)	):
				if i in dup_set:
						continue

				for j in range(i + 1   , len(lowercase__	)	):
						if j in dup_set:
								continue
						if _is_equal_tensor_proto(inits[i]   , inits[j]	):
								dup_set.add(lowercase__	)
								dup_set.add(lowercase__	)

								__lowercase=			inits[j].data_type
								__lowercase=			numpy.prod(inits[j].dims	)
								if dtype == 1:
										mem_size *= 4
								elif dtype == 6:
										mem_size *= 4
								elif dtype == 7 or dtype == 1_1:
										mem_size *= 8
								else:
										print('unexpected data type: '   , lowercase__	)
								total_reduced_size += mem_size

								__lowercase=			inits[i].name
								__lowercase=			inits[j].name

								if name_i in dup_map:
										dup_map[name_i].append(lowercase__	)
								else:
										__lowercase=			[name_j]
								ind_to_replace.append((j, i)	)

		print('total reduced size: '   , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4   , 'GB'	)

		__lowercase=			sorted(lowercase__	)
		_remove_dup_initializers_from_model(lowercase__   , lowercase__   , lowercase__	)

		__lowercase=			'optimized_' + model_file_name
		__lowercase=			os.path.join(lowercase__   , lowercase__	)
		onnx.save(lowercase__   , lowercase__	)

		return new_model






