# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType


class   lowercase      (       a       ):




							def __init__(  self				:   Optional[int]		,							_UpperCamelCase				:   int )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    data





							def __iter__(  self				:   Tuple )   ->			Optional[int]:



												'''simple docstring'''

												for element in self.data:
																	yield element





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any]=True					):
					SCREAMING_SNAKE_CASE           =    Accelerator(even_batches=UpperCAmelCase__					)
					assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
					return accelerator





def 		__lowerCamelCase							(UpperCAmelCase__       :							Accelerator ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							bool = False					):
					if iterable:
										SCREAMING_SNAKE_CASE           =    DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase__					)					)					)
					else:
										SCREAMING_SNAKE_CASE           =    TensorDataset(torch.as_tensor(range(UpperCAmelCase__					)					)					)

					SCREAMING_SNAKE_CASE           =    DataLoader(UpperCAmelCase__ ,	batch_size=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    accelerator.prepare(UpperCAmelCase__					)

					return dl





def 		__lowerCamelCase							(UpperCAmelCase__       :							Accelerator ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							List[int] ,	UpperCAmelCase__       :							List[int] ,	):
					SCREAMING_SNAKE_CASE           =    create_dataloader(accelerator=UpperCAmelCase__ ,	dataset_size=UpperCAmelCase__ ,	batch_size=UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    [len(batch[0]					) for batch in dl]

					if accelerator.process_index == 0:
										assert batch_sizes == process_0_expected_batch_sizes
					elif accelerator.process_index == 1:
										assert batch_sizes == process_1_expected_batch_sizes





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    create_accelerator()

					# without padding, we would expect a different number of batches
					verify_dataloader_batch_sizes(
					    UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1 ,	process_0_expected_batch_sizes=[1, 1] ,	process_1_expected_batch_sizes=[1, 1] ,	)

					# without padding, we would expect the same number of batches, but different sizes
					verify_dataloader_batch_sizes(
					    UpperCAmelCase__ ,	dataset_size=7 ,	batch_size=2 ,	process_0_expected_batch_sizes=[2, 2] ,	process_1_expected_batch_sizes=[2, 2] ,	)





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    create_accelerator(even_batches=UpperCAmelCase__					)

					verify_dataloader_batch_sizes(
					    UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1 ,	process_0_expected_batch_sizes=[1, 1] ,	process_1_expected_batch_sizes=[1] ,	)

					verify_dataloader_batch_sizes(
					    UpperCAmelCase__ ,	dataset_size=7 ,	batch_size=2 ,	process_0_expected_batch_sizes=[2, 2] ,	process_1_expected_batch_sizes=[2, 1] ,	)





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    create_accelerator(even_batches=UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    torch.nn.Linear(1 ,	1					)
					SCREAMING_SNAKE_CASE           =    accelerator.prepare(UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    create_dataloader(UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1					)

					SCREAMING_SNAKE_CASE           =    []
					with accelerator.join_uneven_inputs([ddp_model]					):
										for batch_idx, batch in enumerate(UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    ddp_model(batch[0].float()					)
															SCREAMING_SNAKE_CASE           =    output.sum()
															loss.backward()
															batch_idxs.append(UpperCAmelCase__					)

					accelerator.wait_for_everyone()

					if accelerator.process_index == 0:
										assert batch_idxs == [0, 1]
					elif accelerator.process_index == 1:
										assert batch_idxs == [0]





def 		__lowerCamelCase							(UpperCAmelCase__       :							int					):
					with warnings.catch_warnings(record=UpperCAmelCase__					) as w:
										with accelerator.join_uneven_inputs([Mock()]					):
															pass

										assert issubclass(w[-1].category ,	UpperCAmelCase__					)
										assert "only supported for multi-GPU" in str(w[-1].message					)





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    True
					SCREAMING_SNAKE_CASE           =    False
					SCREAMING_SNAKE_CASE           =    create_accelerator(even_batches=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    torch.nn.Linear(1 ,	1					)
					SCREAMING_SNAKE_CASE           =    accelerator.prepare(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    create_dataloader(UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1					)
					SCREAMING_SNAKE_CASE           =    create_dataloader(UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1					)

					with accelerator.join_uneven_inputs([ddp_model] ,	even_batches=UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    train_dl.batch_sampler.even_batches
										SCREAMING_SNAKE_CASE           =    valid_dl.batch_sampler.even_batches

					assert train_dl_overridden_value == overridden_even_batches
					assert valid_dl_overridden_value == overridden_even_batches
					assert train_dl.batch_sampler.even_batches == default_even_batches
					assert valid_dl.batch_sampler.even_batches == default_even_batches





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    True
					SCREAMING_SNAKE_CASE           =    False
					SCREAMING_SNAKE_CASE           =    create_accelerator(even_batches=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    torch.nn.Linear(1 ,	1					)
					SCREAMING_SNAKE_CASE           =    accelerator.prepare(UpperCAmelCase__					)
					create_dataloader(UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1 ,	iterable=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    create_dataloader(UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1					)

					with warnings.catch_warnings():
										warnings.filterwarnings("ignore"					)
										try:
															with accelerator.join_uneven_inputs([ddp_model] ,	even_batches=UpperCAmelCase__					):
																				SCREAMING_SNAKE_CASE           =    batch_dl.batch_sampler.even_batches
										except AttributeError:
															# ensure attribute error is not raised when processing iterable dl
															raise AssertionError

					assert batch_dl_overridden_value == overridden_even_batches
					assert batch_dl.batch_sampler.even_batches == default_even_batches





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    create_accelerator()
					SCREAMING_SNAKE_CASE           =    torch.nn.Linear(1 ,	1					)
					SCREAMING_SNAKE_CASE           =    accelerator.prepare(UpperCAmelCase__					)
					create_dataloader(UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1 ,	iterable=UpperCAmelCase__					)

					with warnings.catch_warnings(record=UpperCAmelCase__					) as w:
										with accelerator.join_uneven_inputs([ddp_model] ,	even_batches=UpperCAmelCase__					):
															pass

										assert issubclass(w[-1].category ,	UpperCAmelCase__					)
										assert "only supported for map-style datasets" in str(w[-1].message					)





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    create_accelerator()

					accelerator.print("Test that even_batches variable ensures uniform batches across processes"					)
					test_default_ensures_even_batch_sizes()

					accelerator.print("Run tests with even_batches disabled"					)
					test_can_disable_even_batches()

					accelerator.print("Test joining uneven inputs"					)
					test_can_join_uneven_inputs()

					accelerator.print("Test overriding even_batches when joining uneven inputs"					)
					test_join_can_override_even_batches()

					accelerator.print("Test overriding even_batches for mixed dataloader types"					)
					test_join_can_override_for_mixed_type_dataloaders()

					accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders"					)
					test_join_raises_warning_for_iterable_when_overriding_even_batches()

					accelerator.print("Test join with non DDP distributed raises warning"					)
					SCREAMING_SNAKE_CASE           =    accelerator.state.distributed_type
					SCREAMING_SNAKE_CASE           =    DistributedType.FSDP
					test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    original_state


if __name__ == "__main__":
							main()





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowerCamelCase   :				Optional[Any]     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict					):
					try:
										with open(UpperCAmelCase__ ,	"rb"					) as flax_state_f:
															SCREAMING_SNAKE_CASE           =    from_bytes(UpperCAmelCase__ ,	flax_state_f.read()					)
					except UnpicklingError as e:
										try:
															with open(UpperCAmelCase__					) as f:
																				if f.read().startswith("version"					):
																									raise OSError(
																									    "You seem to have cloned a repository without having git-lfs installed. Please"
																									    " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
																									    " folder you cloned."					)
																				else:
																									raise ValueError from e
										except (UnicodeDecodeError, ValueError):
															raise EnvironmentError(F"Unable to convert {model_file} to Flax deserializable object. "					)

					return load_flax_weights_in_pytorch_model(UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
										    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
										    " instructions."					)
										raise

					# check if we have bf16 weights
					SCREAMING_SNAKE_CASE           =    flatten_dict(jax.tree_util.tree_map(lambda UpperCAmelCase__					: x.dtype == jnp.bfloataa ,	UpperCAmelCase__					)					).values()
					if any(UpperCAmelCase__					):
										# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
										    "before loading those in PyTorch model."					)
										SCREAMING_SNAKE_CASE           =    jax.tree_util.tree_map(
										    lambda UpperCAmelCase__					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params ,	UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    ""

					SCREAMING_SNAKE_CASE           =    flatten_dict(UpperCAmelCase__ ,	sep="."					)
					SCREAMING_SNAKE_CASE           =    pt_model.state_dict()

					# keep track of unexpected & missing keys
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    set(pt_model_dict.keys()					)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										SCREAMING_SNAKE_CASE           =    flax_key_tuple.split("."					)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]
															SCREAMING_SNAKE_CASE           =    jnp.transpose(UpperCAmelCase__ ,	(3, 2, 0, 1)					)
										elif flax_key_tuple_array[-1] == "kernel":
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]
															SCREAMING_SNAKE_CASE           =    flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(UpperCAmelCase__					):
																				SCREAMING_SNAKE_CASE           =    (
																				    flax_key_tuple_string.replace("_0" ,	".0"					)
																				    .replace("_1" ,	".1"					)
																				    .replace("_2" ,	".2"					)
																				    .replace("_3" ,	".3"					)
																				    .replace("_4" ,	".4"					)
																				    .replace("_5" ,	".5"					)
																				    .replace("_6" ,	".6"					)
																				    .replace("_7" ,	".7"					)
																				    .replace("_8" ,	".8"					)
																				    .replace("_9" ,	".9"					)
																				)

										SCREAMING_SNAKE_CASE           =    ".".join(UpperCAmelCase__					)

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
																				    F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."					)
															else:
																				# add weight to pytorch dict
																				SCREAMING_SNAKE_CASE           =    np.asarray(UpperCAmelCase__					) if not isinstance(UpperCAmelCase__ ,	np.ndarray					) else flax_tensor
																				SCREAMING_SNAKE_CASE           =    torch.from_numpy(UpperCAmelCase__					)
																				# remove from missing keys
																				missing_keys.remove(UpperCAmelCase__					)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(UpperCAmelCase__					)

					pt_model.load_state_dict(UpperCAmelCase__					)

					# re-transform missing_keys to list
					SCREAMING_SNAKE_CASE           =    list(UpperCAmelCase__					)

					if len(UpperCAmelCase__					) > 0:
										logger.warning(
										    "Some weights of the Flax model were not used when initializing the PyTorch model"
										    F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
										    F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
										    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
										    F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
										    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
										    " FlaxBertForSequenceClassification model)."					)
					if len(UpperCAmelCase__					) > 0:
										logger.warning(
										    F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
										    F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
										    " use it for predictions and inference."					)

					return pt_model




