





import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text


def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			[]
    for line in lines:
        SCREAMING_SNAKE_CASE__											=			re.sub(r'''#.*''' ,							'''''' ,							lowerCAmelCase_	)  # remove comments
        if line:
            filtered_lines.append(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			'''\n'''.join(lowerCAmelCase_	)

    # Make a hash from all this code
    SCREAMING_SNAKE_CASE__											=			full_str.encode('''utf-8'''	)
    return shaaaa(lowerCAmelCase_	).hexdigest()


# get importable module names and hash for caching
_A					:				str				=			{
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_A					:				Union[str, Any]				=			{
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_A					:				List[str]				=			{"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
_A					:				Dict[str, List[str]]				=			{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
  _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")






import os

import numpy
import onnx





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      a.name
			SCREAMING_SNAKE_CASE_   : Dict					  =      b.name

			SCREAMING_SNAKE_CASE_   : str					  =      ''
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      ''

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      a == b

			SCREAMING_SNAKE_CASE_   : str					  =      name_a
			SCREAMING_SNAKE_CASE_   : str					  =      name_b

			return res





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Dict  )       ->  Optional[int]:







			"""simple docstring"""





			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        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Dict  )       ->  List[Any]:







			"""simple docstring"""





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





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Any  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      list(model.graph.initializer  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      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_   : List[str]					  =      inits[i].name
						SCREAMING_SNAKE_CASE_   : str					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Tuple					  =      os.path.dirname(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      os.path.basename(lowerCamelCase_  )

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

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

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

			SCREAMING_SNAKE_CASE_   : int					  =      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_  )

												SCREAMING_SNAKE_CASE_   : int					  =      inits[j].data_type
												SCREAMING_SNAKE_CASE_   : str					  =      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: '  ,							lowerCamelCase_  )
												total_reduced_size += mem_size

												SCREAMING_SNAKE_CASE_   : int					  =      inits[i].name
												SCREAMING_SNAKE_CASE_   : List[Any]					  =      inits[j].name

												if name_i in dup_map:
															dup_map[name_i].append(lowerCamelCase_  )
												else:
															SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [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_   : int					  =      sorted(lowerCamelCase_  )
			_remove_dup_initializers_from_model(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'optimized_' + model_file_name
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      os.path.join(lowerCamelCase_  ,							lowerCamelCase_  )
			onnx.save(lowerCamelCase_  ,							lowerCamelCase_  )

			return new_model



