


"""simple docstring"""


import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map




def   snake_case		(     UpperCamelCase__       :						Union[dict, list, tuple, torch.Tensor]    )   ->      List[Tuple[int, ...]]:
      lowerCamelCase					:					Optional[int]        =	[]
      if isinstance(UpperCamelCase__			,				UpperCamelCase__    ):
            for v in tree.values():
                  shapes.extend(_fetch_dims(UpperCamelCase__    )    )
      elif isinstance(UpperCamelCase__			,				(list, tuple)    ):
            for t in tree:
                  shapes.extend(_fetch_dims(UpperCamelCase__    )    )
      elif isinstance(UpperCamelCase__			,				torch.Tensor    ):
            shapes.append(tree.shape    )
      else:
            raise ValueError("""Not supported"""    )

      return shapes




@torch.jit.ignore
def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						Tuple[int, ...]    )   ->      Tuple[int, ...]:
      lowerCamelCase					:					str        =	[]
      for d in reversed(UpperCamelCase__    ):
            idx.append(flat_idx % d    )
            lowerCamelCase					:					Any        =	flat_idx // d

      return tuple(reversed(UpperCamelCase__    )    )




@torch.jit.ignore
def   snake_case		(     UpperCamelCase__       :						Sequence[int]			,				UpperCamelCase__       :						Sequence[int]			,				UpperCamelCase__       :						Sequence[int]			,				UpperCamelCase__       :						Optional[Sequence[bool]] = None			,				UpperCamelCase__       :						Optional[Sequence[bool]] = None			,				)   ->      List[Tuple[slice, ...]]:

      # start_edges and end_edges both indicate whether, starting from any given
      # dimension, the start/end index is at the top/bottom edge of the
      # corresponding tensor, modeled as a tree
      def reduce_edge_list(UpperCamelCase__       :						List[bool]    ) -> None:
            lowerCamelCase					:					str        =	True
            for i in range(len(UpperCamelCase__    )    ):
                  lowerCamelCase					:					int        =	-1 * (i + 1)
                  l[reversed_idx] &= tally
                  lowerCamelCase					:					Dict        =	l[reversed_idx]

      if start_edges is None:
            lowerCamelCase					:					Optional[int]        =	[s == 0 for s in start]
            reduce_edge_list(UpperCamelCase__    )
      if end_edges is None:
            lowerCamelCase					:					List[str]        =	[e == (d - 1) for e, d in zip(UpperCamelCase__			,				UpperCamelCase__    )]
            reduce_edge_list(UpperCamelCase__    )

      # Base cases. Either start/end are empty and we're done, or the final,
      # one-dimensional tensor can be simply sliced
      if len(UpperCamelCase__    ) == 0:
            return [()]
      elif len(UpperCamelCase__    ) == 1:
            return [(slice(start[0]			,				end[0] + 1    ),)]

      lowerCamelCase					:					List[Tuple[slice, ...]]        =	[]
      lowerCamelCase					:					List[slice]        =	[]

      # Dimensions common to start and end can be selected directly
      for s, e in zip(UpperCamelCase__			,				UpperCamelCase__    ):
            if s == e:
                  path_list.append(slice(UpperCamelCase__			,				s + 1    )    )
            else:
                  break

      lowerCamelCase					:					Tuple[slice, ...]        =	tuple(UpperCamelCase__    )
      lowerCamelCase					:					List[str]        =	len(UpperCamelCase__    )

      # start == end, and we're done
      if divergence_idx == len(UpperCamelCase__    ):
            return [path]

      def upper() -> Tuple[Tuple[slice, ...], ...]:
            assert start_edges is not None
            assert end_edges is not None

            lowerCamelCase					:					Optional[Any]        =	start[divergence_idx]
            return tuple(
                path + (slice(UpperCamelCase__			,				sdi + 1    ),) + s
                for s in _get_minimal_slice_set(
                    start[divergence_idx + 1 :]			,				[d - 1 for d in dims[divergence_idx + 1 :]]			,				dims[divergence_idx + 1 :]			,				start_edges=start_edges[divergence_idx + 1 :]			,				end_edges=[True for _ in end_edges[divergence_idx + 1 :]]			,				)    )

      def lower() -> Tuple[Tuple[slice, ...], ...]:
            assert start_edges is not None
            assert end_edges is not None

            lowerCamelCase					:					str        =	end[divergence_idx]
            return tuple(
                path + (slice(UpperCamelCase__			,				edi + 1    ),) + s
                for s in _get_minimal_slice_set(
                    [0 for _ in start[divergence_idx + 1 :]]			,				end[divergence_idx + 1 :]			,				dims[divergence_idx + 1 :]			,				start_edges=[True for _ in start_edges[divergence_idx + 1 :]]			,				end_edges=end_edges[divergence_idx + 1 :]			,				)    )

      # If both start and end are at the edges of the subtree rooted at
      # divergence_idx, we can just select the whole subtree at once
      if start_edges[divergence_idx] and end_edges[divergence_idx]:
            slices.append(path + (slice(start[divergence_idx]			,				end[divergence_idx] + 1    ),)    )
      # If just start is at the edge, we can grab almost all of the subtree,
      # treating only the ragged bottom edge as an edge case
      elif start_edges[divergence_idx]:
            slices.append(path + (slice(start[divergence_idx]			,				end[divergence_idx]    ),)    )
            slices.extend(lower()    )
      # Analogous to the previous case, but the top is ragged this time
      elif end_edges[divergence_idx]:
            slices.extend(upper()    )
            slices.append(path + (slice(start[divergence_idx] + 1			,				end[divergence_idx] + 1    ),)    )
      # If both sides of the range are ragged, we need to handle both sides
      # separately. If there's contiguous meat in between them, we can index it
      # in one big chunk
      else:
            slices.extend(upper()    )
            lowerCamelCase					:					Optional[Any]        =	end[divergence_idx] - start[divergence_idx]
            if middle_ground > 1:
                  slices.append(path + (slice(start[divergence_idx] + 1			,				end[divergence_idx]    ),)    )
            slices.extend(lower()    )

      return slices




@torch.jit.ignore
def   snake_case		(     UpperCamelCase__       :						torch.Tensor			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						int    )   ->      torch.Tensor:
      lowerCamelCase					:					str        =	t.shape[:no_batch_dims]
      lowerCamelCase					:					Dict        =	list(_flat_idx_to_idx(UpperCamelCase__			,				UpperCamelCase__    )    )
      # _get_minimal_slice_set is inclusive
      lowerCamelCase					:					List[str]        =	list(_flat_idx_to_idx(flat_end - 1			,				UpperCamelCase__    )    )

      # Get an ordered list of slices to perform
      lowerCamelCase					:					Any        =	_get_minimal_slice_set(
          UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				)

      lowerCamelCase					:					Any        =	[t[s] for s in slices]

      return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]    ) for s in sliced_tensors]    )




def   snake_case		(     UpperCamelCase__       :						Callable			,				UpperCamelCase__       :						Dict[str, Any]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						bool = False			,				UpperCamelCase__       :						Any = None			,				UpperCamelCase__       :						bool = False			,				)   ->      Any:
      if not (len(UpperCamelCase__    ) > 0):
            raise ValueError("""Must provide at least one input"""    )

      lowerCamelCase					:					str        =	[shape[:no_batch_dims] for shape in _fetch_dims(UpperCamelCase__    )]
      lowerCamelCase					:					Any        =	tuple([max(UpperCamelCase__    ) for s in zip(*UpperCamelCase__    )]    )

      def _prep_inputs(UpperCamelCase__       :						torch.Tensor    ) -> torch.Tensor:
            if not low_mem:
                  if not sum(t.shape[:no_batch_dims]    ) == no_batch_dims:
                        lowerCamelCase					:					List[Any]        =	t.expand(orig_batch_dims + t.shape[no_batch_dims:]    )
                  lowerCamelCase					:					Union[str, Any]        =	t.reshape(-1			,				*t.shape[no_batch_dims:]    )
            else:
                  lowerCamelCase					:					Optional[Any]        =	t.expand(orig_batch_dims + t.shape[no_batch_dims:]    )
            return t

      lowerCamelCase					:					Dict[str, Any]        =	tensor_tree_map(_prep_inputs			,				UpperCamelCase__    )
      lowerCamelCase					:					str        =	None
      if _out is not None:
            lowerCamelCase					:					Union[str, Any]        =	tensor_tree_map(lambda UpperCamelCase__    : t.view([-1] + list(t.shape[no_batch_dims:]    )    )			,				_out    )

      lowerCamelCase					:					int        =	1
      for d in orig_batch_dims:
            flat_batch_dim *= d

      lowerCamelCase					:					Optional[Any]        =	flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

      def _select_chunk(UpperCamelCase__       :						torch.Tensor    ) -> torch.Tensor:
            return t[i : i + chunk_size] if t.shape[0] != 1 else t

      lowerCamelCase					:					int        =	0
      lowerCamelCase					:					Optional[int]        =	prepped_outputs
      for _ in range(UpperCamelCase__    ):
            # Chunk the input
            if not low_mem:
                  lowerCamelCase					:					str        =	_select_chunk
            else:
                  lowerCamelCase					:					Union[str, Any]        =	partial(
                      _chunk_slice			,				flat_start=UpperCamelCase__			,				flat_end=min(UpperCamelCase__			,				i + chunk_size    )			,				no_batch_dims=len(UpperCamelCase__    )			,				)

            lowerCamelCase					:					Dict[str, Any]        =	tensor_tree_map(UpperCamelCase__			,				UpperCamelCase__    )

            # Run the layer on the chunk
            lowerCamelCase					:					Any        =	layer(**UpperCamelCase__    )

            # Allocate space for the output
            if out is None:
                  lowerCamelCase					:					List[str]        =	tensor_tree_map(lambda UpperCamelCase__    : t.new_zeros((flat_batch_dim,) + t.shape[1:]    )			,				UpperCamelCase__    )

            # Put the chunk in its pre-allocated space
            if isinstance(UpperCamelCase__			,				UpperCamelCase__    ):

                  def assign(UpperCamelCase__       :						dict			,				UpperCamelCase__       :						dict    ) -> None:
                        for k, v in da.items():
                              if isinstance(UpperCamelCase__			,				UpperCamelCase__    ):
                                    assign(UpperCamelCase__			,				da[k]    )
                              else:
                                    if _add_into_out:
                                          v[i : i + chunk_size] += da[k]
                                    else:
                                          lowerCamelCase					:					List[Any]        =	da[k]

                  assign(UpperCamelCase__			,				UpperCamelCase__    )
            elif isinstance(UpperCamelCase__			,				UpperCamelCase__    ):
                  for xa, xa in zip(UpperCamelCase__			,				UpperCamelCase__    ):
                        if _add_into_out:
                              xa[i : i + chunk_size] += xa
                        else:
                              lowerCamelCase					:					Union[str, Any]        =	xa
            elif isinstance(UpperCamelCase__			,				torch.Tensor    ):
                  if _add_into_out:
                        out[i : i + chunk_size] += output_chunk
                  else:
                        lowerCamelCase					:					List[Any]        =	output_chunk
            else:
                  raise ValueError("""Not supported"""    )

            i += chunk_size

      lowerCamelCase					:					Optional[int]        =	tensor_tree_map(lambda UpperCamelCase__    : t.view(orig_batch_dims + t.shape[1:]    )			,				UpperCamelCase__    )

      return out






class 			A__	:


     """simple docstring"""

     def __init__(	self:						List[str]     ,		__a:						int = 512     ,		)->    Optional[int]:
           lowerCamelCase					:					List[Any]        =	max_chunk_size
           lowerCamelCase					:					Optional[int]        =	None
           lowerCamelCase					:					Optional[tuple]        =	None

     def 		a__						(	self:						Union[str, Any]     ,		__a:						Callable     ,		__a:						tuple     ,		__a:						int	)->    int:
           logging.info("""Tuning chunk size..."""	)

           if min_chunk_size >= self.max_chunk_size:
                 return min_chunk_size

           lowerCamelCase					:					List[int]        =	[2**l for l in range(int(math.log(self.max_chunk_size     ,		2	)	) + 1	)]
           lowerCamelCase					:					Union[str, Any]        =	[c for c in candidates if c > min_chunk_size]
           lowerCamelCase					:					Dict        =	[min_chunk_size] + candidates
           candidates[-1] += 4

           def test_chunk_size(__a:						int	) -> bool:
                 try:
                       with torch.no_grad():
                             fn(*__a     ,		chunk_size=__a	)
                       return True
                 except RuntimeError:
                       return False

           lowerCamelCase					:					Optional[Any]        =	0
           lowerCamelCase					:					Optional[int]        =	len(__a	) - 1
           while i > min_viable_chunk_size_index:
                 lowerCamelCase					:					List[str]        =	test_chunk_size(candidates[i]	)
                 if not viable:
                       lowerCamelCase					:					int        =	(min_viable_chunk_size_index + i) // 2
                 else:
                       lowerCamelCase					:					Union[str, Any]        =	i
                       lowerCamelCase					:					Dict        =	(i + len(__a	) - 1) // 2

           return candidates[min_viable_chunk_size_index]

     def 		a__						(	self:						Dict     ,		__a:						Iterable     ,		__a:						Iterable	)->    bool:
           lowerCamelCase					:					Dict        =	True
           for aa, aa in zip(__a     ,		__a	):
                 assert type(__a	) == type(__a	)
                 if isinstance(__a     ,		(list, tuple)	):
                       consistent &= self._compare_arg_caches(__a     ,		__a	)
                 elif isinstance(__a     ,		__a	):
                       lowerCamelCase					:					str        =	[v for _, v in sorted(aa.items()     ,		key=lambda __a	: x[0]	)]
                       lowerCamelCase					:					List[str]        =	[v for _, v in sorted(aa.items()     ,		key=lambda __a	: x[0]	)]
                       consistent &= self._compare_arg_caches(__a     ,		__a	)
                 else:
                       consistent &= aa == aa

           return consistent







     def 		a__						(	self:						Tuple     ,		__a:						Callable     ,		__a:						tuple     ,		__a:						int     ,		)->    int:
           lowerCamelCase					:					List[str]        =	True
           lowerCamelCase					:					tuple        =	tree_map(lambda __a	: a.shape if isinstance(__a     ,		torch.Tensor	) else a     ,		__a     ,		__a	)
           if self.cached_arg_data is not None:
                 # If args have changed shape/value, we need to re-tune
                 assert len(self.cached_arg_data	) == len(__a	)
                 lowerCamelCase					:					Any        =	self._compare_arg_caches(self.cached_arg_data     ,		__a	)
           else:
                 # Otherwise, we can reuse the precomputed value
                 lowerCamelCase					:					int        =	False

           if not consistent:
                 lowerCamelCase					:					Optional[Any]        =	self._determine_favorable_chunk_size(
                     __a     ,		__a     ,		__a     ,		)
                 lowerCamelCase					:					List[Any]        =	arg_data

           assert self.cached_chunk_size is not None

           return self.cached_chunk_size








"""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 			A__	:


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Tuple     ,		__a:						Optional[int]	)->    List[str]:
           return None


class 			A__	:


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						Tuple     ,		__a:						str     ,		__a:						str     ,		__a:						str	)->    Tuple:
           return None


class 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Optional[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:						Optional[Any]	)->    int:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(__a     ,		"""tf"""     ,		12     ,		**__a	)

     @require_torch
     @slow
     def 		a__						(	self:						str	)->    int:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(__a     ,		"""pt"""     ,		12     ,		**__a	)

     @require_torch
     @slow
     def 		a__						(	self:						Union[str, Any]	)->    Dict:
           from transformers import BertModel

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

           with TemporaryDirectory() as bert_save_dir:
                 lowerCamelCase					:					List[str]        =	BertModel(BertConfig(vocab_size=len(__a	)	)	)
                 model.save_pretrained(__a	)
                 self._test_export(__a     ,		"""pt"""     ,		12     ,		__a	)

     @require_tf
     @slow
     def 		a__						(	self:						Optional[Any]	)->    Optional[int]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCamelCase					:					Optional[int]        =	self._test_export(__a     ,		"""tf"""     ,		12     ,		**__a	)
                 lowerCamelCase					:					Tuple        =	quantize(Path(__a	)	)

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

     @require_torch
     @slow
     def 		a__						(	self:						Any	)->    Optional[int]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCamelCase					:					Any        =	self._test_export(__a     ,		"""pt"""     ,		12     ,		**__a	)
                 lowerCamelCase					:					Dict        =	quantize(__a	)

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

     def 		a__						(	self:						List[Any]     ,		__a:						Optional[Any]     ,		__a:						List[Any]     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]=None     ,		**__a:						Optional[int]	)->    Any:
           try:
                 # Compute path
                 with TemporaryDirectory() as tempdir:
                       lowerCamelCase					:					Optional[Any]        =	Path(__a	).joinpath("""model.onnx"""	)

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

                 # Export
                 convert(__a     ,		__a     ,		__a     ,		__a     ,		__a     ,		**__a	)

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

     @require_torch
     @require_tokenizers
     @slow
     def 		a__						(	self:						Tuple	)->    Dict:
           from transformers import BertModel

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

     @require_tf
     @require_tokenizers
     @slow
     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           from transformers import TFBertModel

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

     def 		a__						(	self:						List[str]     ,		__a:						str     ,		__a:						Optional[Any]     ,		__a:						str	)->    List[Any]:
           lowerCamelCase					:					List[str]        =	FeatureExtractionPipeline(__a     ,		__a	)

           lowerCamelCase					:					List[str]        =	["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	infer_shapes(__a     ,		__a	)

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

           # 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:						List[Any]	)->    int:
           lowerCamelCase					:					List[str]        =	["""input_ids""", """attention_mask""", """token_type_ids"""]
           lowerCamelCase					:					str        =	{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	ensure_valid_input(FuncContiguousArgs()     ,		__a     ,		__a	)

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

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

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

           # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	ensure_valid_input(FuncNonContiguousArgs()     ,		__a     ,		__a	)

           # Should have exactly the one arg (all before the one not provided "some_other_args")
           self.assertEqual(len(__a	)     ,		1	)
           self.assertEqual(len(__a	)     ,		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:						Tuple	)->    Tuple:
           lowerCamelCase					:					Optional[int]        =	generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""	)     ,		"""-test"""	)
           self.assertEqual("""/home/something/my_fake_model-test.onnx"""     ,		generated.as_posix()	)




