


"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCamelCase__				:int			   =	logging.get_logger(__name__)





def 				A_       ( snake_case__		)					->  List[int]:
			if isinstance(snake_case__       ,					np.ndarray		):
						return list(tensor.shape		)

			_UpperCamelCase					:List[str]					     =	tf.shape(snake_case__		)

			if tensor.shape == tf.TensorShape(snake_case__		):
						return dynamic

			_UpperCamelCase					:Optional[Any]					     =	tensor.shape.as_list()

			return [dynamic[i] if s is None else s for i, s in enumerate(snake_case__		)]





def 				A_       ( snake_case__       ,					snake_case__ = None       ,					snake_case__ = None		)					->  tf.Tensor:
			return tf.nn.softmax(logits=logits + 1E-9       ,					axis=snake_case__       ,					name=snake_case__		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__=1E-5       ,					snake_case__=-1		)					->  Dict:
			# This is a very simplified functional layernorm, designed to duplicate
			# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
			# models in Transformers.

			if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case__       ,					snake_case__		):
						raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''		)

			# Get mean and variance on the axis to be normalized
			_UpperCamelCase     ,      _UpperCamelCase					:int					     =	tf.nn.moments(snake_case__       ,					axes=[axis]       ,					keepdims=snake_case__		)

			if axis != -1:
						# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
						# on every dimension except axis
						_UpperCamelCase					:List[Any]					     =	[1] * inputs.shape.rank
						_UpperCamelCase					:str					     =	shape_list(snake_case__		)[axis]
						_UpperCamelCase					:Optional[int]					     =	tf.reshape(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Optional[int]					     =	tf.reshape(snake_case__       ,					snake_case__		)

			# Compute layer normalization using the batch_normalization
			# function.
			_UpperCamelCase					:Optional[Any]					     =	tf.nn.batch_normalization(
			    snake_case__       ,					snake_case__       ,					snake_case__       ,					offset=snake_case__       ,					scale=snake_case__       ,					variance_epsilon=snake_case__       ,					)
			return outputs





def 				A_       ( snake_case__       ,					snake_case__=0       ,					snake_case__=-1		)					->  List[str]:
			# Replicates the behavior of torch.flatten in TF

			# If end_dim or start_dim is negative, count them from the end
			if end_dim < 0:
						end_dim += input.shape.rank
			if start_dim < 0:
						start_dim += input.shape.rank

			if start_dim == end_dim:
						return input

			_UpperCamelCase					:Dict					     =	tf.shape(snake_case__		)
			_UpperCamelCase					:int					     =	tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]		)
			_UpperCamelCase					:str					     =	tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]       ,					axis=0		)
			return tf.reshape(snake_case__       ,					snake_case__		)





def 				A_       ( snake_case__		)					->  tf.Tensor:
			if not isinstance(snake_case__       ,					tf.Tensor		):
						_UpperCamelCase					:int					     =	tf.convert_to_tensor(snake_case__		)  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						_UpperCamelCase					:int					     =	encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						_UpperCamelCase					:Dict					     =	encoder_attention_mask[:, None, None, :]
			# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
			# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
			# /transformer/transformer_layers.py#L270
			# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
			# encoder_extended_attention_mask.transpose(-1, -2))
			_UpperCamelCase					:List[Any]					     =	(
			    tf.cast(1       ,					encoder_attention_mask.dtype		) - encoder_extended_attention_mask
			) * encoder_extended_attention_mask.dtype.min

			return encoder_extended_attention_mask





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__ = "input_ids"		)					->  None:
			tf.debugging.assert_less(
			    snake_case__       ,					tf.cast(snake_case__       ,					dtype=tensor.dtype		)       ,					message=(
			        f"The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case__		)}) must be smaller than the embedding "
			        f"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
			    )       ,					)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  str:
			_UpperCamelCase					:str					     =	6_45_12
			# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
			# because in that case even chunking the array would not make the saving
			# possible.
			_UpperCamelCase					:List[str]					     =	[x for x in data if len(snake_case__		) > HDF5_OBJECT_HEADER_LIMIT]

			# Expecting this to never be true.
			if bad_attributes:
						raise RuntimeError(
						    '''The following attributes cannot be saved to HDF5 file because '''
						    f"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
						    f"bytes: {bad_attributes}"		)

			_UpperCamelCase					:int					     =	np.asarray(snake_case__		)

			_UpperCamelCase					:Union[str, Any]					     =	1
			_UpperCamelCase					:Tuple					     =	np.array_split(snake_case__       ,					snake_case__		)

			# This will never loop forever thanks to the test above.
			while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data		):
						num_chunks += 1
						_UpperCamelCase					:Tuple					     =	np.array_split(snake_case__       ,					snake_case__		)

			if num_chunks > 1:
						for chunk_id, chunk_data in enumerate(snake_case__		):
									_UpperCamelCase					:Tuple					     =	chunk_data
			else:
						_UpperCamelCase					:Tuple					     =	data





def 				A_       ( snake_case__       ,					snake_case__		)					->  Union[str, Any]:
			if name in group.attrs:
						_UpperCamelCase					:Dict					     =	[n.decode('''utf8'''		) if hasattr(snake_case__       ,					'''decode'''		) else n for n in group.attrs[name]]
			else:
						_UpperCamelCase					:int					     =	[]
						_UpperCamelCase					:List[Any]					     =	0
						while "%s%d" % (name, chunk_id) in group.attrs:
									data.extend(
									    [n.decode('''utf8'''		) if hasattr(snake_case__       ,					'''decode'''		) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]		)
									chunk_id += 1
			return data





def 				A_       ( snake_case__		)					->  Optional[Any]:

			def _expand_single_ad_tensor(snake_case__		):
						if isinstance(snake_case__       ,					tf.Tensor		) and t.shape.rank == 1:
									return tf.expand_dims(snake_case__       ,					axis=-1		)
						return t

			return tf.nest.map_structure(_expand_single_ad_tensor       ,					snake_case__		)





"""simple docstring"""






import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


UpperCamelCase__				:Dict			   =	"""true"""





def 				A_       ( snake_case__       ,					snake_case__=82       ,					snake_case__=16		)					->  List[Any]:
			set_seed(42		)
			_UpperCamelCase					:Dict					     =	RegressionModel()
			_UpperCamelCase					:Dict					     =	deepcopy(snake_case__		)
			_UpperCamelCase					:List[Any]					     =	RegressionDataset(length=snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	DataLoader(snake_case__       ,					batch_size=snake_case__		)
			model.to(accelerator.device		)
			_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	accelerator.prepare(snake_case__       ,					snake_case__		)
			return model, ddp_model, dataloader





def 				A_       ( snake_case__       ,					snake_case__=False		)					->  Optional[int]:
			_UpperCamelCase					:Optional[Any]					     =	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''		)
			_UpperCamelCase					:int					     =	load_dataset('''glue'''       ,					'''mrpc'''       ,					split='''validation'''		)

			def tokenize_function(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	tokenizer(examples['''sentence1''']       ,					examples['''sentence2''']       ,					truncation=snake_case__       ,					max_length=snake_case__		)
						return outputs

			with accelerator.main_process_first():
						_UpperCamelCase					:Optional[int]					     =	dataset.map(
						    snake_case__       ,					batched=snake_case__       ,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,					)

			_UpperCamelCase					:Tuple					     =	tokenized_datasets.rename_column('''label'''       ,					'''labels'''		)

			def collate_fn(snake_case__		):
						if use_longest:
									return tokenizer.pad(snake_case__       ,					padding='''longest'''       ,					return_tensors='''pt'''		)
						return tokenizer.pad(snake_case__       ,					padding='''max_length'''       ,					max_length=1_28       ,					return_tensors='''pt'''		)

			return DataLoader(snake_case__       ,					shuffle=snake_case__       ,					collate_fn=snake_case__       ,					batch_size=16		)





def 				A_       ( snake_case__       ,					snake_case__		)					->  Tuple:
			_UpperCamelCase					:Union[str, Any]					     =	Accelerator(dispatch_batches=snake_case__       ,					split_batches=snake_case__		)
			_UpperCamelCase					:Any					     =	get_dataloader(snake_case__       ,					not dispatch_batches		)
			_UpperCamelCase					:Optional[int]					     =	AutoModelForSequenceClassification.from_pretrained(
			    '''hf-internal-testing/mrpc-bert-base-cased'''       ,					return_dict=snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:int					     =	accelerator.prepare(snake_case__       ,					snake_case__		)
			return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  List[Any]:
			_UpperCamelCase					:Any					     =	[]
			for batch in dataloader:
						_UpperCamelCase     ,      _UpperCamelCase					:List[Any]					     =	batch.values()
						with torch.no_grad():
									_UpperCamelCase					:List[Any]					     =	model(snake_case__		)
									_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	accelerator.gather_for_metrics((logit, target)		)
									logits_and_targets.append((logit, target)		)
			_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	[], []
			for logit, targ in logits_and_targets:
						logits.append(snake_case__		)
						targs.append(snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	torch.cat(snake_case__		), torch.cat(snake_case__		)
			return logits, targs





def 				A_       ( snake_case__       ,					snake_case__=82       ,					snake_case__=False       ,					snake_case__=False       ,					snake_case__=16		)					->  Dict:
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	get_basic_setup(snake_case__       ,					snake_case__       ,					snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	generate_predictions(snake_case__       ,					snake_case__       ,					snake_case__		)
			assert (
			    len(snake_case__		) == num_samples
			), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case__		)}"





def 				A_       ( snake_case__ = False       ,					snake_case__ = False		)					->  Optional[Any]:
			_UpperCamelCase					:Any					     =	evaluate.load('''glue'''       ,					'''mrpc'''		)
			_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	get_mrpc_setup(snake_case__       ,					snake_case__		)
			# First do baseline
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	setup['''no''']
			model.to(snake_case__		)
			model.eval()
			for batch in dataloader:
						batch.to(snake_case__		)
						with torch.inference_mode():
									_UpperCamelCase					:str					     =	model(**snake_case__		)
						_UpperCamelCase					:Dict					     =	outputs.logits.argmax(dim=-1		)
						metric.add_batch(predictions=snake_case__       ,					references=batch['''labels''']		)
			_UpperCamelCase					:int					     =	metric.compute()

			# Then do distributed
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	setup['''ddp''']
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									_UpperCamelCase					:Dict					     =	model(**snake_case__		)
						_UpperCamelCase					:str					     =	outputs.logits.argmax(dim=-1		)
						_UpperCamelCase					:Union[str, Any]					     =	batch['''labels''']
						_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	accelerator.gather_for_metrics((preds, references)		)
						metric.add_batch(predictions=snake_case__       ,					references=snake_case__		)
			_UpperCamelCase					:Optional[Any]					     =	metric.compute()

			for key in "accuracy f1".split():
						assert math.isclose(
						    baseline[key]       ,					distributed[key]		), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"





def 				A_       ( )					->  Optional[Any]:
			_UpperCamelCase					:int					     =	Accelerator(split_batches=snake_case__       ,					dispatch_batches=snake_case__		)
			if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_warning()
			else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			# These are a bit slower so they should only be ran on the GPU or TPU
			if torch.cuda.is_available() or is_tpu_available():
						if accelerator.is_local_main_process:
									print('''**Testing gather_for_metrics**'''		)
						for split_batches in [True, False]:
									for dispatch_batches in [True, False]:
												if accelerator.is_local_main_process:
															print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"		)
												test_mrpc(snake_case__       ,					snake_case__		)
												accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('''**Test torch metrics**'''		)
			for split_batches in [True, False]:
						for dispatch_batches in [True, False]:
									_UpperCamelCase					:Tuple					     =	Accelerator(split_batches=snake_case__       ,					dispatch_batches=snake_case__		)
									if accelerator.is_local_main_process:
												print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"		)
									test_torch_metrics(snake_case__       ,					99		)
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('''**Test last batch is not dropped when perfectly divisible**'''		)
			_UpperCamelCase					:Any					     =	Accelerator()
			test_torch_metrics(snake_case__       ,					5_12		)
			accelerator.state._reset_state()





def 				A_       ( snake_case__		)					->  Dict:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
					main()

