


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       lowercase_		(		_A			:    Union[dict, list, tuple, torch.Tensor]						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  []
      if isinstance(__lowerCAmelCase    ,					__lowerCAmelCase						):
            for v in tree.values():
                  shapes.extend(_fetch_dims(__lowerCAmelCase						)						)
      elif isinstance(__lowerCAmelCase    ,					(list, tuple)						):
            for t in tree:
                  shapes.extend(_fetch_dims(__lowerCAmelCase						)						)
      elif isinstance(__lowerCAmelCase    ,					torch.Tensor						):
            shapes.append(tree.shape						)
      else:
            raise ValueError("Not supported"						)

      return shapes

@torch.jit.ignore
def       lowercase_		(		_A			:    int    ,					_A			:    Tuple[int, ...]						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  []
      for d in reversed(__lowerCAmelCase						):
            idx.append(flat_idx % d						)
            lowerCamelCase__		:				Tuple    =  flat_idx // d

      return tuple(reversed(__lowerCAmelCase						)						)

@torch.jit.ignore
def       lowercase_		(		_A			:    Sequence[int]    ,					_A			:    Sequence[int]    ,					_A			:    Sequence[int]    ,					_A			:    Optional[Sequence[bool]] = None    ,					_A			:    Optional[Sequence[bool]] = None    ,					):

      """simple docstring"""




      def reduce_edge_list(_A			:    List[bool]						) -> None:
            lowerCamelCase__		:				str    =  True
            for i in range(len(__lowerCAmelCase						)						):
                  lowerCamelCase__		:				Union[str, Any]    =  -1 * (i + 1)
                  l[reversed_idx] &= tally
                  lowerCamelCase__		:				Tuple    =  l[reversed_idx]

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

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

      lowerCamelCase__		:				Dict    =  []
      lowerCamelCase__		:				int    =  []

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

      lowerCamelCase__		:				Union[str, Any]    =  tuple(__lowerCAmelCase						)
      lowerCamelCase__		:				int    =  len(__lowerCAmelCase						)

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

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

            lowerCamelCase__		:				List[Any]    =  start[divergence_idx]
            return tuple(
                path + (slice(__lowerCAmelCase    ,					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__		:				Any    =  end[divergence_idx]
            return tuple(
                path + (slice(__lowerCAmelCase    ,					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__		:				Union[str, 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       lowercase_		(		_A			:    torch.Tensor    ,					_A			:    int    ,					_A			:    int    ,					_A			:    int						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  t.shape[:no_batch_dims]
      lowerCamelCase__		:				Any    =  list(_flat_idx_to_idx(__lowerCAmelCase    ,					__lowerCAmelCase						)						)
      # _get_minimal_slice_set is inclusive
      lowerCamelCase__		:				List[Any]    =  list(_flat_idx_to_idx(flat_end - 1    ,					__lowerCAmelCase						)						)

      # Get an ordered list of slices to perform
      lowerCamelCase__		:				int    =  _get_minimal_slice_set(
          __lowerCAmelCase    ,					__lowerCAmelCase    ,					__lowerCAmelCase    ,					)

      lowerCamelCase__		:				Union[str, Any]    =  [t[s] for s in slices]

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

def       lowercase_		(		_A			:    Callable    ,					_A			:    Dict[str, Any]    ,					_A			:    int    ,					_A			:    int    ,					_A			:    bool = False    ,					_A			:    Any = None    ,					_A			:    bool = False    ,					):

      """simple docstring"""




      if not (len(__lowerCAmelCase						) > 0):
            raise ValueError("Must provide at least one input"						)

      lowerCamelCase__		:				Tuple    =  [shape[:no_batch_dims] for shape in _fetch_dims(__lowerCAmelCase						)]
      lowerCamelCase__		:				List[str]    =  tuple([max(__lowerCAmelCase						) for s in zip(*__lowerCAmelCase						)]						)

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

      lowerCamelCase__		:				Dict    =  tensor_tree_map(_prep_inputs    ,					__lowerCAmelCase						)
      lowerCamelCase__		:				List[str]    =  None
      if _out is not None:
            lowerCamelCase__		:				Tuple    =  tensor_tree_map(lambda _A						: t.view([-1] + list(t.shape[no_batch_dims:]						)						)    ,					_out						)

      lowerCamelCase__		:				Tuple    =  1
      for d in orig_batch_dims:
            flat_batch_dim *= d

      lowerCamelCase__		:				Optional[int]    =  flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

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

            lowerCamelCase__		:				Optional[int]    =  tensor_tree_map(__lowerCAmelCase    ,					__lowerCAmelCase						)

            # Run the layer on the chunk
            lowerCamelCase__		:				Optional[Any]    =  layer(**__lowerCAmelCase						)

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

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

                  def assign(_A			:    dict    ,					_A			:    dict						) -> None:
                        for k, v in da.items():
                              if isinstance(__lowerCAmelCase    ,					__lowerCAmelCase						):
                                    assign(__lowerCAmelCase    ,					da[k]						)
                              else:
                                    if _add_into_out:
                                          v[i : i + chunk_size] += da[k]
                                    else:
                                          lowerCamelCase__		:				str    =  da[k]

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

            i += chunk_size

      lowerCamelCase__		:				Tuple    =  tensor_tree_map(lambda _A						: t.view(orig_batch_dims + t.shape[1:]						)    ,					__lowerCAmelCase						)

      return out







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Optional[Any]					,      __lowerCamelCase							:			Union[str, Any] = 512					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  max_chunk_size
             lowerCamelCase__		:				Dict    =  None
             lowerCamelCase__		:				Any    =  None





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             logging.info("Tuning chunk size..."       )

             if min_chunk_size >= self.max_chunk_size:
                   return min_chunk_size

             lowerCamelCase__		:				int    =  [2**l for l in range(int(math.log(self.max_chunk_size					,      2       )       ) + 1       )]
             lowerCamelCase__		:				Tuple    =  [c for c in candidates if c > min_chunk_size]
             lowerCamelCase__		:				int    =  [min_chunk_size] + candidates
             candidates[-1] += 4

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

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

             return candidates[min_viable_chunk_size_index]





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  True
             for aa, aa in zip(_UpperCamelCase					,      _UpperCamelCase       ):
                   assert type(_UpperCamelCase       ) == type(_UpperCamelCase       )
                   if isinstance(_UpperCamelCase					,      (list, tuple)       ):
                         consistent &= self._compare_arg_caches(_UpperCamelCase					,      _UpperCamelCase       )
                   elif isinstance(_UpperCamelCase					,      _UpperCamelCase       ):
                         lowerCamelCase__		:				Optional[int]    =  [v for _, v in sorted(aa.items()					,      key=lambda __lowerCamelCase       : x[0]       )]
                         lowerCamelCase__		:				Any    =  [v for _, v in sorted(aa.items()					,      key=lambda __lowerCamelCase       : x[0]       )]
                         consistent &= self._compare_arg_caches(_UpperCamelCase					,      _UpperCamelCase       )
                   else:
                         consistent &= aa == aa

             return consistent





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Dict					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  True
             lowerCamelCase__		:				Tuple    =  tree_map(lambda __lowerCamelCase       : a.shape if isinstance(_UpperCamelCase					,      torch.Tensor       ) else a					,      _UpperCamelCase					,      _UpperCamelCase       )
             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(_UpperCamelCase       )
                   lowerCamelCase__		:				List[str]    =  self._compare_arg_caches(self.cached_arg_data					,      _UpperCamelCase       )
             else:
                   # Otherwise, we can reuse the precomputed value
                   lowerCamelCase__		:				Any    =  False

             if not consistent:
                   lowerCamelCase__		:				Tuple    =  self._determine_favorable_chunk_size(
                       _UpperCamelCase					,      _UpperCamelCase					,      _UpperCamelCase					,      )
                   lowerCamelCase__		:				List[str]    =  arg_data

             assert self.cached_chunk_size is not None

             return self.cached_chunk_size




from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = ""				):




			"""simple docstring"""


			lowerCAmelCase_						=	url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
			lowerCAmelCase_						=	BeautifulSoup(requests.get(__lowerCAmelCase				).text    ,							"html.parser"				)
			lowerCAmelCase_						=	soup.find_all("td"    ,							attrs="titleColumn"				)
			lowerCAmelCase_						=	soup.find_all("td"    ,							class_="ratingColumn imdbRating"				)
			return {
			    title.a.text: float(rating.strong.text				)
			    for title, rating in zip(__lowerCAmelCase    ,							__lowerCAmelCase				)
			}






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = "IMDb_Top_250_Movies.csv"				):




			"""simple docstring"""


			lowerCAmelCase_						=	get_imdb_top_aaa_movies()
			with open(__lowerCAmelCase    ,							"w"    ,							newline=""				) as out_file:
						lowerCAmelCase_						=	csv.writer(__lowerCAmelCase				)
						writer.writerow(["Movie title", "IMDb rating"]				)
						for title, rating in movies.items():
									writer.writerow([title, rating]				)


if __name__ == "__main__":
			write_movies()



