

import os

import numpy
import onnx






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    List[Any] 	=			a.name
	__lowerCamelCase    :    Union[str, Any] 	=			b.name

	__lowerCamelCase    :    str 	=			''
	__lowerCamelCase    :    str 	=			''

	__lowerCamelCase    :    Any 	=			a == b

	__lowerCamelCase    :    Tuple 	=			name_a
	__lowerCamelCase    :    Tuple 	=			name_b

	return res






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Any:
	for i, input_name in enumerate(node_proto.input      ):
		if input_name == name:
			node_proto.input.insert(lowerCamelCase__     ,			lowerCamelCase__      )
			node_proto.input.pop(i + 1      )

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	for n in graph_proto.node:
		_node_replace_input_with(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Union[str, Any] 	=			list(model.graph.initializer      )
	__lowerCamelCase    :    Union[str, Any] 	=			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

		__lowerCamelCase    :    Optional[int] 	=			inits[i].name
		__lowerCamelCase    :    List[Any] 	=			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     ,			lowerCamelCase__     ,			lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	__lowerCamelCase    :    Any 	=			os.path.dirname(lowerCamelCase__      )
	__lowerCamelCase    :    List[Any] 	=			os.path.basename(lowerCamelCase__      )

	__lowerCamelCase    :    List[str] 	=			onnx.load(os.path.join(lowerCamelCase__     ,			lowerCamelCase__      )      )

	__lowerCamelCase    :    Any 	=			list(model.graph.initializer      )

	__lowerCamelCase    :    int 	=			set()
	__lowerCamelCase    :    Optional[int] 	=			{}
	__lowerCamelCase    :    Dict 	=			[]

	__lowerCamelCase    :    Optional[Any] 	=			0

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

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

				__lowerCamelCase    :    Optional[Any] 	=			inits[j].data_type
				__lowerCamelCase    :    List[Any] 	=			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: '     ,			lowerCamelCase__      )
				total_reduced_size += mem_size

				__lowerCamelCase    :    Tuple 	=			inits[i].name
				__lowerCamelCase    :    Optional[int] 	=			inits[j].name

				if name_i in dup_map:
					dup_map[name_i].append(lowerCamelCase__      )
				else:
					__lowerCamelCase    :    Tuple 	=			[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'      )

	__lowerCamelCase    :    Tuple 	=			sorted(lowerCamelCase__      )
	_remove_dup_initializers_from_model(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )

	__lowerCamelCase    :    List[str] 	=			'optimized_' + model_file_name
	__lowerCamelCase    :    Dict 	=			os.path.join(lowerCamelCase__     ,			lowerCamelCase__      )
	onnx.save(lowerCamelCase__     ,			lowerCamelCase__      )

	return new_model







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a		={
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RoFormerTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RoFormerForCausalLM""",
					    """RoFormerForMaskedLM""",
					    """RoFormerForMultipleChoice""",
					    """RoFormerForQuestionAnswering""",
					    """RoFormerForSequenceClassification""",
					    """RoFormerForTokenClassification""",
					    """RoFormerLayer""",
					    """RoFormerModel""",
					    """RoFormerPreTrainedModel""",
					    """load_tf_weights_in_roformer""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRoFormerForCausalLM""",
					    """TFRoFormerForMaskedLM""",
					    """TFRoFormerForMultipleChoice""",
					    """TFRoFormerForQuestionAnswering""",
					    """TFRoFormerForSequenceClassification""",
					    """TFRoFormerForTokenClassification""",
					    """TFRoFormerLayer""",
					    """TFRoFormerModel""",
					    """TFRoFormerPreTrainedModel""",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaxRoFormerForMaskedLM""",
					    """FlaxRoFormerForMultipleChoice""",
					    """FlaxRoFormerForQuestionAnswering""",
					    """FlaxRoFormerForSequenceClassification""",
					    """FlaxRoFormerForTokenClassification""",
					    """FlaxRoFormerModel""",
					    """FlaxRoFormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
					from .tokenization_roformer import RoFormerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roformer_fast import RoFormerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roformer import (
										    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RoFormerForCausalLM,
										    RoFormerForMaskedLM,
										    RoFormerForMultipleChoice,
										    RoFormerForQuestionAnswering,
										    RoFormerForSequenceClassification,
										    RoFormerForTokenClassification,
										    RoFormerLayer,
										    RoFormerModel,
										    RoFormerPreTrainedModel,
										    load_tf_weights_in_roformer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roformer import (
										    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRoFormerForCausalLM,
										    TFRoFormerForMaskedLM,
										    TFRoFormerForMultipleChoice,
										    TFRoFormerForQuestionAnswering,
										    TFRoFormerForSequenceClassification,
										    TFRoFormerForTokenClassification,
										    TFRoFormerLayer,
										    TFRoFormerModel,
										    TFRoFormerPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roformer import (
										    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxRoFormerForMaskedLM,
										    FlaxRoFormerForMultipleChoice,
										    FlaxRoFormerForQuestionAnswering,
										    FlaxRoFormerForSequenceClassification,
										    FlaxRoFormerForTokenClassification,
										    FlaxRoFormerModel,
										    FlaxRoFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




