

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 a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			[]
	if isinstance(lowercase__      ,			lowercase__						):
		for v in tree.values():
			shapes.extend(_fetch_dims(lowercase__						)						)
	elif isinstance(lowercase__      ,			(list, tuple)						):
		for t in tree:
			shapes.extend(_fetch_dims(lowercase__						)						)
	elif isinstance(lowercase__      ,			torch.Tensor						):
		shapes.append(tree.shape						)
	else:
		raise ValueError('Not supported'						)

	return shapes





@torch.jit.ignore
def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			[]
	for d in reversed(lowercase__						):
		idx.append(flat_idx % d						)
		snake_case_   		=			flat_idx // d

	return tuple(reversed(lowercase__						)						)





@torch.jit.ignore
def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__ = None      ,			lowercase__ = None      ,			):





	'''simple docstring'''

	# 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(lowercase__						) -> None:
		snake_case_   		=			True
		for i in range(len(lowercase__						)						):
			snake_case_   		=			-1 * (i + 1)
			l[reversed_idx] &= tally
			snake_case_   		=			l[reversed_idx]

	if start_edges is None:
		snake_case_   		=			[s == 0 for s in start]
		reduce_edge_list(lowercase__						)
	if end_edges is None:
		snake_case_   		=			[e == (d - 1) for e, d in zip(lowercase__      ,			lowercase__						)]
		reduce_edge_list(lowercase__						)

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

	snake_case_   		=			[]
	snake_case_   		=			[]

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

	snake_case_   		=			tuple(lowercase__						)
	snake_case_   		=			len(lowercase__						)

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

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

		snake_case_   		=			start[divergence_idx]
		return tuple(
		    path + (slice(lowercase__      ,			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

		snake_case_   		=			end[divergence_idx]
		return tuple(
		    path + (slice(lowercase__      ,			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()						)
		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 a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			t.shape[:no_batch_dims]
	snake_case_   		=			list(_flat_idx_to_idx(lowercase__      ,			lowercase__						)						)
	# _get_minimal_slice_set is inclusive
	snake_case_   		=			list(_flat_idx_to_idx(flat_end - 1      ,			lowercase__						)						)

	# Get an ordered list of slices to perform
	snake_case_   		=			_get_minimal_slice_set(
	    lowercase__      ,			lowercase__      ,			lowercase__      ,			)

	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 a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__ = False      ,			lowercase__ = None      ,			lowercase__ = False      ,			):





	'''simple docstring'''
	if not (len(lowercase__						) > 0):
		raise ValueError('Must provide at least one input'						)

	snake_case_   		=			[shape[:no_batch_dims] for shape in _fetch_dims(lowercase__						)]
	snake_case_   		=			tuple([max(lowercase__						) for s in zip(*lowercase__						)]						)

	def _prep_inputs(lowercase__						) -> torch.Tensor:
		if not low_mem:
			if not sum(t.shape[:no_batch_dims]						) == no_batch_dims:
				snake_case_   		=			t.expand(orig_batch_dims + t.shape[no_batch_dims:]						)
			snake_case_   		=			t.reshape(-1      ,			*t.shape[no_batch_dims:]						)
		else:
			snake_case_   		=			t.expand(orig_batch_dims + t.shape[no_batch_dims:]						)
		return t

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

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

	snake_case_   		=			flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

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

		snake_case_   		=			tensor_tree_map(lowercase__      ,			lowercase__						)

		# Run the layer on the chunk
		snake_case_   		=			layer(**lowercase__						)

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

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

			def assign(lowercase__      ,			lowercase__						) -> None:
				for k, v in da.items():
					if isinstance(lowercase__      ,			lowercase__						):
						assign(lowercase__      ,			da[k]						)
					else:
						if _add_into_out:
							v[i : i + chunk_size] += da[k]
						else:
							snake_case_   		=			da[k]

			assign(lowercase__      ,			lowercase__						)
		elif isinstance(lowercase__      ,			lowercase__						):
			for xa, xa in zip(lowercase__      ,			lowercase__						):
				if _add_into_out:
					xa[i : i + chunk_size] += xa
				else:
					snake_case_   		=			xa
		elif isinstance(lowercase__      ,			torch.Tensor						):
			if _add_into_out:
				out[i : i + chunk_size] += output_chunk
			else:
				snake_case_   		=			output_chunk
		else:
			raise ValueError('Not supported'						)

		i += chunk_size

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

	return out






class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase = 5_12			,					):

				"""simple docstring"""


				snake_case_   		=			max_chunk_size
				snake_case_   		=			None
				snake_case_   		=			None


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


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

				if min_chunk_size >= self.max_chunk_size:
					return min_chunk_size

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

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

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

				return candidates[min_viable_chunk_size_index]


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			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				):
						snake_case_   		=			[v for _, v in sorted(aa.items()			,					key=lambda __UpperCamelCase				: x[0]				)]
						snake_case_   		=			[v for _, v in sorted(aa.items()			,					key=lambda __UpperCamelCase				: x[0]				)]
						consistent &= self._compare_arg_caches(__UpperCamelCase			,					__UpperCamelCase				)
					else:
						consistent &= aa == aa

				return consistent







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					):

				"""simple docstring"""


				snake_case_   		=			True
				snake_case_   		=			tree_map(lambda __UpperCamelCase				: 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				)
					snake_case_   		=			self._compare_arg_caches(self.cached_arg_data			,					__UpperCamelCase				)
				else:
					# Otherwise, we can reuse the precomputed value
					snake_case_   		=			False

				if not consistent:
					snake_case_   		=			self._determine_favorable_chunk_size(
					    __UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					)
					snake_case_   		=			arg_data

				assert self.cached_chunk_size is not None

				return self.cached_chunk_size



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	None
			__A    	=	BloomTokenizerFast
			__A    	=	BloomTokenizerFast
			__A    	=	True
			__A    	=	False
			__A    	=	"""tokenizer_file"""
			__A    	=	{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				super().setUp()
				snake_case_   		=			BloomTokenizerFast.from_pretrained('bigscience/tokenizer'				)
				tokenizer.save_pretrained(self.tmpdirname				)


			def       __lowerCAmelCase			(     self			,					**__UpperCamelCase				):

				"""simple docstring"""


				kwargs.update(self.special_tokens_map				)
				return BloomTokenizerFast.from_pretrained(self.tmpdirname			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_rust_tokenizer()

				snake_case_   		=			['The quick brown fox</s>', 'jumps over the lazy dog</s>']
				snake_case_   		=			[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

				snake_case_   		=			tokenizer.batch_encode_plus(__UpperCamelCase				)['input_ids']
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)

				snake_case_   		=			tokenizer.batch_decode(__UpperCamelCase				)
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase=6				):

				"""simple docstring"""


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
						snake_case_   		=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase			,					**__UpperCamelCase				)
						# tokenizer_r.pad_token = None # Hotfixing padding = None
						# Simple input
						snake_case_   		=			'This is a simple input'
						snake_case_   		=			['This is a simple input 1', 'This is a simple input 2']
						snake_case_   		=			('This is a simple input', 'This is a pair')
						snake_case_   		=			[
						    ('This is a simple input 1', 'This is a simple input 2'),
						    ('This is a simple pair 1', 'This is a simple pair 2'),
						]

						# Simple input tests
						try:
							tokenizer_r.encode(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)

							tokenizer_r.batch_encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.encode(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.batch_encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)
						except ValueError:
							self.fail('Bloom Tokenizer should be able to deal with padding'				)

						snake_case_   		=			None  # Hotfixing padding = None
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Simple input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Simple input
						self.assertRaises(
						    __UpperCamelCase			,					tokenizer_r.batch_encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'			,					)

						# Pair input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Pair input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Pair input
						self.assertRaises(
						    __UpperCamelCase			,					tokenizer_r.batch_encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'			,					)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_rust_tokenizer()
				snake_case_   		=			load_dataset('xnli'			,					'all_languages'			,					split='test'			,					streaming=__UpperCamelCase				)

				snake_case_   		=			next(iter(__UpperCamelCase				)				)['premise']  # pick up one data
				snake_case_   		=			list(sample_data.values()				)

				snake_case_   		=			list(map(tokenizer.encode			,					__UpperCamelCase				)				)
				snake_case_   		=			[tokenizer.decode(__UpperCamelCase			,					clean_up_tokenization_spaces=__UpperCamelCase				) for x in output_tokens]
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)			,					1				)
				self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)			,					1				)
