





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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[dict, list, tuple, torch.Tensor]     ):
		SCREAMING_SNAKE_CASE__								=   []
		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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Tuple[int, ...]     ):
		SCREAMING_SNAKE_CASE__								=   []
		for d in reversed(UpperCamelCase__     ):
				idx.append(flat_idx % d     )
				SCREAMING_SNAKE_CASE__								=   flat_idx // d

		return tuple(reversed(UpperCamelCase__     )     )






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

		# 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:
				SCREAMING_SNAKE_CASE__								=   True
				for i in range(len(UpperCamelCase__     )     ):
						SCREAMING_SNAKE_CASE__								=   -1 * (i + 1)
						l[reversed_idx] &= tally
						SCREAMING_SNAKE_CASE__								=   l[reversed_idx]

		if start_edges is None:
				SCREAMING_SNAKE_CASE__								=   [s == 0 for s in start]
				reduce_edge_list(UpperCamelCase__     )
		if end_edges is None:
				SCREAMING_SNAKE_CASE__								=   [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     ),)]

		SCREAMING_SNAKE_CASE__								=   []
		SCREAMING_SNAKE_CASE__								=   []

		# 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

		SCREAMING_SNAKE_CASE__								=   tuple(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   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

				SCREAMING_SNAKE_CASE__								=   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

				SCREAMING_SNAKE_CASE__								=   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()     )
				SCREAMING_SNAKE_CASE__								=   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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				torch.Tensor       , UpperCamelCase__:				int       , UpperCamelCase__:				int       , UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   t.shape[:no_batch_dims]
		SCREAMING_SNAKE_CASE__								=   list(_flat_idx_to_idx(UpperCamelCase__       , UpperCamelCase__     )     )
		# _get_minimal_slice_set is inclusive
		SCREAMING_SNAKE_CASE__								=   list(_flat_idx_to_idx(flat_end - 1       , UpperCamelCase__     )     )

		# Get an ordered list of slices to perform
		SCREAMING_SNAKE_CASE__								=   _get_minimal_slice_set(
		    UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , )

		SCREAMING_SNAKE_CASE__								=   [t[s] for s in slices]

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






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

		SCREAMING_SNAKE_CASE__								=   [shape[:no_batch_dims] for shape in _fetch_dims(UpperCamelCase__     )]
		SCREAMING_SNAKE_CASE__								=   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:
								SCREAMING_SNAKE_CASE__								=   t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
						SCREAMING_SNAKE_CASE__								=   t.reshape(-1       , *t.shape[no_batch_dims:]     )
				else:
						SCREAMING_SNAKE_CASE__								=   t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
				return t

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

		SCREAMING_SNAKE_CASE__								=   1
		for d in orig_batch_dims:
				flat_batch_dim *= d

		SCREAMING_SNAKE_CASE__								=   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

		SCREAMING_SNAKE_CASE__								=   0
		SCREAMING_SNAKE_CASE__								=   prepped_outputs
		for _ in range(UpperCamelCase__     ):
				# Chunk the input
				if not low_mem:
						SCREAMING_SNAKE_CASE__								=   _select_chunk
				else:
						SCREAMING_SNAKE_CASE__								=   partial(
						    _chunk_slice       , flat_start=UpperCamelCase__       , flat_end=min(UpperCamelCase__       , i + chunk_size     )       , no_batch_dims=len(UpperCamelCase__     )       , )

				SCREAMING_SNAKE_CASE__								=   tensor_tree_map(UpperCamelCase__       , UpperCamelCase__     )

				# Run the layer on the chunk
				SCREAMING_SNAKE_CASE__								=   layer(**UpperCamelCase__     )

				# Allocate space for the output
				if out is None:
						SCREAMING_SNAKE_CASE__								=   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:
														SCREAMING_SNAKE_CASE__								=   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:
										SCREAMING_SNAKE_CASE__								=   xa
				elif isinstance(UpperCamelCase__       , torch.Tensor     ):
						if _add_into_out:
								out[i : i + chunk_size] += output_chunk
						else:
								SCREAMING_SNAKE_CASE__								=   output_chunk
				else:
						raise ValueError("""Not supported"""     )

				i += chunk_size

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

		return out




class 	UpperCamelCase_							:


							def __init__(				self     :Optional[Any]				,					__A     :int = 512				,					) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   max_chunk_size
									SCREAMING_SNAKE_CASE__								=   None
									SCREAMING_SNAKE_CASE__								=   None


							def   _snake_case	(				self     :Dict				,					__A     :Callable				,					__A     :tuple				,					__A     :int		) -> int:



									"""simple docstring"""



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

									if min_chunk_size >= self.max_chunk_size:
											return min_chunk_size

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

									SCREAMING_SNAKE_CASE__								=   0
									SCREAMING_SNAKE_CASE__								=   len(__A		) - 1
									while i > min_viable_chunk_size_index:
											SCREAMING_SNAKE_CASE__								=   test_chunk_size(candidates[i]		)
											if not viable:
													SCREAMING_SNAKE_CASE__								=   (min_viable_chunk_size_index + i) // 2
											else:
													SCREAMING_SNAKE_CASE__								=   i
													SCREAMING_SNAKE_CASE__								=   (i + len(__A		) - 1) // 2

									return candidates[min_viable_chunk_size_index]


							def   _snake_case	(				self     :Dict				,					__A     :Iterable				,					__A     :Iterable		) -> bool:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   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		):
													SCREAMING_SNAKE_CASE__								=   [v for _, v in sorted(aa.items()				,					key=lambda __A		: x[0]		)]
													SCREAMING_SNAKE_CASE__								=   [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   _snake_case	(				self     :int				,					__A     :Callable				,					__A     :tuple				,					__A     :int				,					) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   True
									SCREAMING_SNAKE_CASE__								=   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		)
											SCREAMING_SNAKE_CASE__								=   self._compare_arg_caches(self.cached_arg_data				,					__A		)
									else:
											# Otherwise, we can reuse the precomputed value
											SCREAMING_SNAKE_CASE__								=   False

									if not consistent:
											SCREAMING_SNAKE_CASE__								=   self._determine_favorable_chunk_size(
											    __A				,					__A				,					__A				,					)
											SCREAMING_SNAKE_CASE__								=   arg_data

									assert self.cached_chunk_size is not None

									return self.cached_chunk_size




'''simple docstring'''





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= BlenderbotSmallTokenizer
 A__				= False
 def A_   (   self						:							Any  )      ->   str:







       '''simple docstring'''
       super().setUp()

       __snake_case :   Any													=    ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
       __snake_case :   str													=    dict(zip(__a      ,     range(len(__a  )  )  )  )

       __snake_case :   List[Any]													=    ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
       __snake_case :   Tuple													=    {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

       __snake_case :   str													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']  )
       __snake_case :   Union[str, Any]													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['merges_file']  )
       with open(self.vocab_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write(json.dumps(__a  ) + '\n'  )
       with open(self.merges_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write('\n'.join(__a  )  )
 def A_   (   self						:							Tuple      ,     **__a						:							Optional[int]  )      ->   List[str]:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							int      ,     __a						:							int  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    'adapt act apte'
       __snake_case :   List[str]													=    'adapt act apte'
       return input_text, output_text
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       __snake_case :   Dict													=    BlenderbotSmallTokenizer(self.vocab_file      ,     self.merges_file      ,     **self.special_tokens_map  )
       __snake_case :   List[str]													=    'adapt act apte'
       __snake_case :   List[Any]													=    ['adapt', 'act', 'ap@@', 'te']
       __snake_case :   Optional[int]													=    tokenizer.tokenize(__a  )
       self.assertListEqual(__a      ,     __a  )

       __snake_case :   List[str]													=    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

       __snake_case :   List[str]													=    [0, 1, 2, 3, 4, 5]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  )      ,     __a  )
 def A_   (   self						:							int  )      ->   str:







       '''simple docstring'''
       __snake_case :   int													=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
       assert tok('sam'  ).input_ids == [1384]
       __snake_case :   List[str]													=    'I am a small frog.'
       __snake_case :   List[Any]													=    tok([src_text]      ,     padding=__a      ,     truncation=__a  )['input_ids']
       __snake_case :   int													=    tok.batch_decode(__a      ,     skip_special_tokens=__a      ,     clean_up_tokenization_spaces=__a  )[0]
       assert src_text != decoded  # I wish it did!
       assert decoded == "i am a small frog ."





 def A_   (   self						:							List[str]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
       __snake_case :   Optional[Any]													=    'I am a small frog .'
       __snake_case :   List[str]													=    '.'
       __snake_case :   List[str]													=    tok(__a  )['input_ids']
       __snake_case :   str													=    tok(__a  )['input_ids']

       assert encoded[-1] == encoded_dot[0]



