
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__		)							->							List[Tuple[int, ...]]:
    """simple docstring"""

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

    return shapes



@torch.jit.ignore
def 						lowercase_      (       A__			,		A__		)							->							Tuple[int, ...]:
    """simple docstring"""

    snake_case								= []
    for d in reversed(A__		):
        idx.append(flat_idx % d		)
        snake_case								= flat_idx // d

    return tuple(reversed(A__		)		)



@torch.jit.ignore
def 						lowercase_      (       A__			,		A__			,		A__			,		A__ = None			,		A__ = None			,		)							->							List[Tuple[slice, ...]]:
    """simple docstring"""

    def reduce_edge_list(A__		) -> None:
        snake_case								= True
        for i in range(len(A__		)		):
            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(A__		)
    if end_edges is None:
        snake_case								= [e == (d - 1) for e, d in zip(A__			,		A__		)]
        reduce_edge_list(A__		)

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

    snake_case								= tuple(A__		)
    snake_case								= len(A__		)

    # start == end, and we're done
    if divergence_idx == len(A__		):
        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(A__			,		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(A__			,		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 						lowercase_      (       A__			,		A__			,		A__			,		A__		)							->							torch.Tensor:
    """simple docstring"""

    snake_case								= t.shape[:no_batch_dims]
    snake_case								= list(_flat_idx_to_idx(A__			,		A__		)		)
    # _get_minimal_slice_set is inclusive
    snake_case								= list(_flat_idx_to_idx(flat_end - 1			,		A__		)		)

    # Get an ordered list of slices to perform
    snake_case								= _get_minimal_slice_set(
        A__			,		A__			,		A__			,		)

    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 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__ = False			,		A__ = None			,		A__ = False			,		)							->							Any:
    """simple docstring"""

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

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

    def _prep_inputs(A__		) -> 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			,		A__		)
    snake_case								= None
    if _out is not None:
        snake_case								= tensor_tree_map(lambda A__		: 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(A__		) -> 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(A__		):
        # Chunk the input
        if not low_mem:
            snake_case								= _select_chunk
        else:
            snake_case								= partial(
                _chunk_slice			,		flat_start=A__			,		flat_end=min(A__			,		i + chunk_size		)			,		no_batch_dims=len(A__		)			,		)

        snake_case								= tensor_tree_map(A__			,		A__		)

        # Run the layer on the chunk
        snake_case								= layer(**A__		)

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

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

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

            assign(A__			,		A__		)
        elif isinstance(A__			,		A__		):
            for xa, xa in zip(A__			,		A__		):
                if _add_into_out:
                    xa[i : i + chunk_size] += xa
                else:
                    snake_case								= xa
        elif isinstance(A__			,		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 A__		: t.view(orig_batch_dims + t.shape[1:]		)			,		A__		)

    return out







class 					lowerCamelCase		:





     def __init__(self				:   Optional[int]    ,   _A				:   int = 5_1_2    ,   )						->						int:
         snake_case								= max_chunk_size
         snake_case								= None
         snake_case								= None





     def 		UpperCAmelCase(self				:   Optional[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

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

         snake_case								= 0
         snake_case								= len(_A       ) - 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(_A       ) - 1) // 2

         return candidates[min_viable_chunk_size_index]





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   Iterable    ,   _A				:   Iterable       )						->						bool:
         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       ):
                 snake_case								= [v for _, v in sorted(aa.items()    ,   key=lambda _A       : x[0]       )]
                 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 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   Callable    ,   _A				:   tuple    ,   _A				:   int    ,   )						->						int:
         snake_case								= True
         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       )
             snake_case								= self._compare_arg_caches(self.cached_arg_data    ,   _A       )
         else:
             # Otherwise, we can reuse the precomputed value
             snake_case								= False

         if not consistent:
             snake_case								= self._determine_favorable_chunk_size(
                 _A    ,   _A    ,   _A    ,   )
             snake_case								= arg_data

         assert self.cached_chunk_size is not None

         return self.cached_chunk_size



import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_A   =			"bert-base-cased"
_A   =			"google/pegasus-xsum"
_A   =			[" Sam ate lunch today.", "Sams lunch ingredients."]
_A   =			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_A   =			"patrickvonplaten/t5-tiny-random"
_A   =			"sshleifer/bart-tiny-random"
_A   =			"sshleifer/tiny-mbart"
_A   =			"sshleifer/tiny-marian-en-de"



def 						lowercase_      (       A__			,		A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case								= "\n".join(A__		)
    Path(A__		).open("w"		).writelines(A__		)



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(A__			,		F'{split}.source'		)			,		A__		)
        _dump_articles(os.path.join(A__			,		F'{split}.target'		)			,		A__		)
    return tmp_dir







class 					lowerCamelCase		(  A_  ):





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     @slow
     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   List[str]       )						->						Optional[int]:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= 8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         snake_case					,							snake_case								= "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   src_lang=_A    ,   tgt_lang=_A    ,   )
         snake_case								= DataLoader(_A    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert isinstance(_A    ,   _A       )
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_src_len
             # show that targets are the same len
             assert batch["labels"].shape[1] == max_tgt_len
             if tok_name != MBART_TINY:
                 continue
             # check language codes in correct place
             snake_case								= shift_tokens_right(batch["labels"]    ,   tokenizer.pad_token_id       )
             assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
             assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

             break  # No need to test every batch





     @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
     def 		UpperCAmelCase(self				:   str    ,   _A				:   Dict       )						->						Dict:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= LegacySeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=2_0    ,   max_target_length=_A    ,   )
         snake_case								= DataLoader(_A    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_len_source
             assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
             # show that targets were truncated
             assert batch["labels"].shape[1] == trunc_target  # Truncated
             assert max_len_target > trunc_target  # Truncated
             break  # No need to test every batch





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[Any]:
         snake_case								= AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         snake_case								= tmp_dir.joinpath("train.source"       ).open().readlines()
         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(_A    ,   _A    ,   1_2_8    ,   _A       )
         snake_case								= {x.name for x in tmp_dir.iterdir()}
         snake_case								= {x.name for x in save_dir.iterdir()}
         snake_case								= save_dir.joinpath("train.source"       ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(_A       ) < len(_A       )
         assert len(_A       ) == 1
         assert len(packed_examples[0]       ) == sum(len(_A       ) for x in orig_examples       )
         assert orig_paths == new_paths





     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,   reason="This test requires fairseq"       )
     def 		UpperCAmelCase(self				:   Optional[int]       )						->						Union[str, Any]:
         if not FAIRSEQ_AVAILABLE:
             return
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=6_4       )
         snake_case								= 6_4
         snake_case								= ds.make_dynamic_sampler(_A    ,   required_batch_size_multiple=_A       )
         snake_case								= [len(_A       ) for x in batch_sampler]
         assert len(set(_A       )       ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(_A       ) == len(_A       )  # no dropped or added examples
         snake_case								= DataLoader(_A    ,   batch_sampler=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= []
         snake_case								= []
         for batch in data_loader:
             snake_case								= batch["input_ids"].shape
             snake_case								= src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             snake_case								= np.product(batch["input_ids"].shape       )
             num_src_per_batch.append(_A       )
             if num_src_tokens > (max_tokens * 1.1):
                 failures.append(_A       )
         assert num_src_per_batch[0] == max(_A       )
         if failures:
             raise AssertionError(f'too many tokens in {len(_A       )} batches'       )





     def 		UpperCAmelCase(self				:   int       )						->						str:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=5_1_2       )
         snake_case								= 2
         snake_case								= ds.make_sortish_sampler(_A    ,   shuffle=_A       )

         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2    ,   sampler=_A       )

         snake_case								= tokenizer.pad_token_id

         def count_pad_tokens(_A				:   Dict    ,   _A				:   Union[str, Any]="input_ids"       ):
             return [batch[k].eq(_A       ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(_A    ,   k="labels"       )       ) < sum(count_pad_tokens(_A    ,   k="labels"       )       )
         assert sum(count_pad_tokens(_A       )       ) < sum(count_pad_tokens(_A       )       )
         assert len(_A       ) == len(_A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Union[str, Any]=1_0_0_0    ,   _A				:   Optional[int]=1_2_8       )						->						List[Any]:
         if os.getenv("USE_REAL_DATA"    ,   _A       ):
             snake_case								= "examples/seq2seq/wmt_en_ro"
             snake_case								= max_len * 2 * 6_4
             if not Path(_A       ).joinpath("train.len"       ).exists():
                 save_len_file(_A    ,   _A       )
         else:
             snake_case								= "examples/seq2seq/test_data/wmt_en_ro"
             snake_case								= max_len * 4
             save_len_file(_A    ,   _A       )

         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   n_obs=_A    ,   )
         return ds, max_tokens, tokenizer





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Union[str, Any]:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset()
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=0    ,   add_extra_examples=_A       )       )
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=1    ,   add_extra_examples=_A       )       )
         assert idsa.intersection(_A       ) == set()





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Optional[Any]       )						->						Union[str, Any]:
         snake_case								= AutoTokenizer.from_pretrained(_A    ,   use_fast=_A       )
         if tok_name == MBART_TINY:
             snake_case								= SeqaSeqDataset(
                 _A    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   src_lang="EN"    ,   tgt_lang="FR"    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
             snake_case								= SeqaSeqDataset(
                 _A    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
             assert len(_A       ) == 1 if tok_name == BART_TINY else len(_A       ) == 0

