

import os

import numpy
import onnx







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       a.name
						SCREAMING_SNAKE_CASE_: int  		=       b.name

						SCREAMING_SNAKE_CASE_: Any  		=       ""
						SCREAMING_SNAKE_CASE_: Tuple  		=       ""

						SCREAMING_SNAKE_CASE_: List[Any]  		=       a == b

						SCREAMING_SNAKE_CASE_: Any  		=       name_a
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       name_b

						return res







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

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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						for n in graph_proto.node:
												_node_replace_input_with(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       list(model.graph.initializer    )
						SCREAMING_SNAKE_CASE_: Optional[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

												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       inits[i].name
												SCREAMING_SNAKE_CASE_: Union[str, 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				,	_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       os.path.dirname(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Any  		=       os.path.basename(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: int  		=       onnx.load(os.path.join(_UpperCAmelCase				,	_UpperCAmelCase    )    )

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

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       set()
						SCREAMING_SNAKE_CASE_: Any  		=       {}
						SCREAMING_SNAKE_CASE_: Any  		=       []

						SCREAMING_SNAKE_CASE_: int  		=       0

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

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

																								SCREAMING_SNAKE_CASE_: List[str]  		=       inits[j].data_type
																								SCREAMING_SNAKE_CASE_: Any  		=       numpy.prod(inits[j].dims    )
																								if dtype == 1:
																														mem_size *= 4
																								elif dtype == 6:
																														mem_size *= 4
																								elif dtype == 7 or dtype == 11:
																														mem_size *= 8
																								else:
																														print("unexpected data type: "				,	_UpperCAmelCase    )
																								total_reduced_size += mem_size

																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       inits[i].name
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       inits[j].name

																								if name_i in dup_map:
																														dup_map[name_i].append(_UpperCAmelCase    )
																								else:
																														SCREAMING_SNAKE_CASE_: List[Any]  		=       [name_j]
																								ind_to_replace.append((j, i)    )

						print("total reduced size: "				,	total_reduced_size / 10_24 / 10_24 / 10_24				,	"GB"    )

						SCREAMING_SNAKE_CASE_: List[Any]  		=       sorted(_UpperCAmelCase    )
						_remove_dup_initializers_from_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       "optimized_" + model_file_name
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       os.path.join(_UpperCAmelCase				,	_UpperCAmelCase    )
						onnx.save(_UpperCAmelCase				,	_UpperCAmelCase    )

						return new_model









import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
						import torch



@is_pipeline_test
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			_UpperCAmelCase   :							str										=     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Any  		=       TextaTextGenerationPipeline(model=lowerCAmelCase__   ,						tokenizer=lowerCAmelCase__)
									return generator, ["Something to write", "Something else"]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there")
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ANY(lowerCAmelCase__)}])
									# These are encoder decoder, they don't just append to incoming string
									self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))

									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator(["This is great !", "Something else"]   ,						num_return_sequences=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

									SCREAMING_SNAKE_CASE_: Dict  		=       generator(
									    ["This is great !", "Something else"]   ,						num_return_sequences=2   ,						batch_size=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

									with self.assertRaises(lowerCAmelCase__):
															generator(4)



			@require_torch
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="pt")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       3
									SCREAMING_SNAKE_CASE_: Any  		=       generator(
									    "Something there"   ,						num_return_sequences=lowerCAmelCase__   ,						num_beams=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Any  		=       [
									    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
									    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
									    {"generated_text": ""},
									]
									self.assertEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       generator("This is a test"   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						return_tensors=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        {"generated_token_ids": ANY(torch.Tensor)},
									        {"generated_token_ids": ANY(torch.Tensor)},
									    ]   ,						)
									SCREAMING_SNAKE_CASE_: str  		=       generator.model.config.eos_token_id
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       "<pad>"
									SCREAMING_SNAKE_CASE_: Tuple  		=       generator(
									    ["This is a test", "This is a second test"]   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						batch_size=2   ,						return_tensors=lowerCAmelCase__   ,						)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [
									            {"generated_token_ids": ANY(torch.Tensor)},
									            {"generated_token_ids": ANY(torch.Tensor)},
									        ],
									        [
									            {"generated_token_ids": ANY(torch.Tensor)},
									            {"generated_token_ids": ANY(torch.Tensor)},
									        ],
									    ]   ,						)




			@require_tf
			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="tf")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])






