'''simple docstring'''







from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel


def    lowerCamelCase       (			UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  Any				,						UpperCAmelCase__				:  Dict=None				,						UpperCAmelCase__				:  List[Any]=None		)			->	int:


							'''simple docstring'''






							if attention_mask is None:
														SCREAMING_SNAKE_CASE__     :Tuple						    = tf.cast(tf.math.not_equal(UpperCAmelCase__				,						config.pad_token_id		)				,						tf.inta		)
							return {"input_ids": input_ids, "attention_mask": attention_mask}




@require_tf
class _SCREAMING_SNAKE_CASE:
						A_			:							int     		= OPTConfig
						A_			:							Union[str, Any]     		= {}
						A_			:							Dict     		= 'gelu'




						def __init__(    self :       Dict							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       Any=13							,							UpperCamelCase_ :       Any=7							,							UpperCamelCase_ :       str=True							,							UpperCamelCase_ :       Any=False							,							UpperCamelCase_ :       Optional[Any]=99							,							UpperCamelCase_ :       List[Any]=16							,							UpperCamelCase_ :       Optional[Any]=2							,							UpperCamelCase_ :       List[Any]=4							,							UpperCamelCase_ :       Any=4							,							UpperCamelCase_ :       Optional[Any]="gelu"							,							UpperCamelCase_ :       Union[str, Any]=0.1							,							UpperCamelCase_ :       int=0.1							,							UpperCamelCase_ :       int=20							,							UpperCamelCase_ :       str=2							,							UpperCamelCase_ :       Optional[Any]=1							,							UpperCamelCase_ :       int=0							,							UpperCamelCase_ :       Dict=16							,							UpperCamelCase_ :       Optional[int]=16							,							)     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = parent
													SCREAMING_SNAKE_CASE__     :List[Any]						    = batch_size
													SCREAMING_SNAKE_CASE__     :Dict						    = seq_length
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = is_training
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = use_labels
													SCREAMING_SNAKE_CASE__     :int						    = vocab_size
													SCREAMING_SNAKE_CASE__     :Tuple						    = hidden_size
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = num_hidden_layers
													SCREAMING_SNAKE_CASE__     :str						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :str						    = intermediate_size
													SCREAMING_SNAKE_CASE__     :Any						    = hidden_act
													SCREAMING_SNAKE_CASE__     :Dict						    = hidden_dropout_prob
													SCREAMING_SNAKE_CASE__     :Any						    = attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__     :Tuple						    = max_position_embeddings
													SCREAMING_SNAKE_CASE__     :Dict						    = eos_token_id
													SCREAMING_SNAKE_CASE__     :Dict						    = pad_token_id
													SCREAMING_SNAKE_CASE__     :int						    = bos_token_id
													SCREAMING_SNAKE_CASE__     :str						    = embed_dim
													SCREAMING_SNAKE_CASE__     :Tuple						    = word_embed_proj_dim
													SCREAMING_SNAKE_CASE__     :Any						    = False




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Tuple						    = ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )							,							1      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tf.concat([input_ids, eos_tensor]							,							axis=1      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.config_cls(
													    vocab_size=self.vocab_size							,							hidden_size=self.hidden_size							,							num_hidden_layers=self.num_hidden_layers							,							num_attention_heads=self.num_attention_heads							,							ffn_dim=self.intermediate_size							,							dropout=self.hidden_dropout_prob							,							attention_dropout=self.attention_probs_dropout_prob							,							max_position_embeddings=self.max_position_embeddings							,							eos_token_id=self.eos_token_id							,							bos_token_id=self.bos_token_id							,							pad_token_id=self.pad_token_id							,							embed_dim=self.embed_dim							,							word_embed_proj_dim=self.word_embed_proj_dim							,							is_encoder_decoder=UpperCamelCase_							,							**self.config_updates							,							)
													SCREAMING_SNAKE_CASE__     :Dict						    = prepare_opt_inputs_dict(UpperCamelCase_							,							UpperCamelCase_      )
													return config, inputs_dict




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       int      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :List[str]						    = TFOPTModel(config=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = inputs_dict['input_ids']

													SCREAMING_SNAKE_CASE__     :str						    = input_ids[:1, :]
													SCREAMING_SNAKE_CASE__     :str						    = inputs_dict['attention_mask'][:1, :]
													SCREAMING_SNAKE_CASE__     :Any						    = 1

													# first forward pass
													SCREAMING_SNAKE_CASE__     :List[Any]						    = model(UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							use_cache=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = outputs.to_tuple()

													# create hypothetical next token and extent to next_input_ids
													SCREAMING_SNAKE_CASE__     :Tuple						    = ids_tensor((self.batch_size, 3)							,							config.vocab_size      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tf.cast(ids_tensor((self.batch_size, 3)							,							2      )							,							tf.inta      )

													# append to next input_ids and
													SCREAMING_SNAKE_CASE__     :str						    = tf.concat([input_ids, next_tokens]							,							axis=-1      )
													SCREAMING_SNAKE_CASE__     :Any						    = tf.concat([attention_mask, next_attn_mask]							,							axis=-1      )

													SCREAMING_SNAKE_CASE__     :Dict						    = model(UpperCamelCase_							,							attention_mask=UpperCamelCase_      )[0]
													SCREAMING_SNAKE_CASE__     :Any						    = model(UpperCamelCase_							,							attention_mask=UpperCamelCase_							,							past_key_values=UpperCamelCase_      )[0]

													self.parent.assertEqual(next_tokens.shape[1]							,							output_from_past.shape[1]      )

													# select random slice
													SCREAMING_SNAKE_CASE__     :Tuple						    = int(ids_tensor((1,)							,							output_from_past.shape[-1]      )      )
													SCREAMING_SNAKE_CASE__     :Any						    = output_from_no_past[:, -3:, random_slice_idx]
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = output_from_past[:, :, random_slice_idx]

													# test that outputs are equal for slice
													tf.debugging.assert_near(UpperCamelCase_							,							UpperCamelCase_							,							rtol=1e-3      )




@require_tf
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							List[Any]     		= (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
						A_			:							Tuple     		= (TFOPTForCausalLM,) if is_tf_available() else ()
						A_			:							Tuple     		= (
						    {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
						)
						A_			:							int     		= False
						A_			:							int     		= False
						A_			:							Optional[Any]     		= False
						A_			:							int     		= 10




						def      __lowerCamelCase       (    self :       Any      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :int						    = TFOPTModelTester(self      )
													SCREAMING_SNAKE_CASE__     :Any						    = ConfigTester(self							,							config_class=UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Any      )     ->				Optional[Any]:
													self.config_tester.run_common_tests()




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Dict      )     ->				Dict:
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[str]						    = self.model_tester.prepare_config_and_inputs_for_common()

													def _get_word_embedding_weight(UpperCamelCase_ :       str							,							UpperCamelCase_ :       Union[str, Any]      ):
																				if hasattr(UpperCamelCase_							,							'weight'      ):
																											return embedding_layer.weight
																				else:
																											# Here we build the word embeddings weights if not exists.
																											# And then we retry to get the attribute once built.
																											model.build()
																											if hasattr(UpperCamelCase_							,							'weight'      ):
																																		return embedding_layer.weight
																											else:
																																		return None

													for model_class in self.all_model_classes:
																				for size in [config.vocab_size - 10, config.vocab_size + 10]:
																											# build the embeddings
																											SCREAMING_SNAKE_CASE__     :Any						    = model_class(config=UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :List[str]						    = _get_word_embedding_weight(UpperCamelCase_							,							model.get_input_embeddings()      )
																											SCREAMING_SNAKE_CASE__     :int						    = _get_word_embedding_weight(UpperCamelCase_							,							model.get_output_embeddings()      )

																											# reshape the embeddings
																											model.resize_token_embeddings(UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :Dict						    = _get_word_embedding_weight(UpperCamelCase_							,							model.get_input_embeddings()      )
																											SCREAMING_SNAKE_CASE__     :List[str]						    = _get_word_embedding_weight(UpperCamelCase_							,							model.get_output_embeddings()      )

																											# check that the resized embeddings size matches the desired size.
																											SCREAMING_SNAKE_CASE__     :List[Any]						    = size if size is not None else config.vocab_size

																											self.assertEqual(new_input_embeddings.shape[0]							,							UpperCamelCase_      )

																											# check that weights remain the same after resizing
																											SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = True
																											for pa, pa in zip(old_input_embeddings.value()							,							new_input_embeddings.value()      ):
																																		if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
																																									SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = False
																											self.assertTrue(UpperCamelCase_      )

																											if old_output_embeddings is not None and new_output_embeddings is not None:
																																		self.assertEqual(new_output_embeddings.shape[0]							,							UpperCamelCase_      )

																																		SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = True
																																		for pa, pa in zip(old_output_embeddings.value()							,							new_output_embeddings.value()      ):
																																									if tf.math.reduce_sum(tf.math.abs(pa - pa      )      ) > 0:
																																																SCREAMING_SNAKE_CASE__     :Dict						    = False
																																		self.assertTrue(UpperCamelCase_      )


def    lowerCamelCase       (			UpperCAmelCase__				:  Union[str, Any]		)			->	Optional[int]:


							'''simple docstring'''






							return tf.constant(UpperCAmelCase__				,						dtype=tf.intaa		)




@require_tf
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):
						A_			:							List[str]     		= 99




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Any						    = tf.ones((4, 1)							,							dtype=tf.intaa      ) * 2
													SCREAMING_SNAKE_CASE__     :List[Any]						    = tf.concat([ids_tensor((4, 6)							,							self.vocab_size - 3      ) + 3, eos_column_vector]							,							axis=1      )
													SCREAMING_SNAKE_CASE__     :int						    = input_ids.shape[0]
													SCREAMING_SNAKE_CASE__     :Any						    = OPTConfig(
													    vocab_size=self.vocab_size							,							hidden_size=24							,							num_hidden_layers=2							,							num_attention_heads=2							,							ffn_dim=32							,							max_position_embeddings=48							,							eos_token_id=2							,							pad_token_id=1							,							bos_token_id=0							,							)
													return config, input_ids, batch_size




@require_sentencepiece
@require_tf
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						@slow
						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Tuple						    = TFOPTModel.from_pretrained('facebook/opt-350m'      )
													SCREAMING_SNAKE_CASE__     :Dict						    = _long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = tf.not_equal(UpperCamelCase_							,							model.config.pad_token_id      )
													with tf.GradientTape():
																				SCREAMING_SNAKE_CASE__     :Any						    = model(input_ids=UpperCamelCase_							,							attention_mask=UpperCamelCase_      ).last_hidden_state
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = (1, 11, 5_12)
													self.assertEqual(output.shape							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = tf.constant(
													    [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]      )
													self.assertTrue(np.allclose(output[:, :3, :3]							,							UpperCamelCase_							,							atol=4e-3      )      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tf.function(UpperCamelCase_							,							jit_compile=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = xla_generate(UpperCamelCase_							,							UpperCamelCase_      )[0]
													self.assertTrue(np.allclose(output[:, :3, :3]							,							UpperCamelCase_							,							atol=4e-2      )      )




@require_tf
@slow
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def      __lowerCamelCase       (    self :       List[str]      )     ->				Any:
													super().setUp()
													SCREAMING_SNAKE_CASE__     :Any						    = 'facebook/opt-350m'




						def      __lowerCamelCase       (    self :       Tuple      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = TFOPTForCausalLM.from_pretrained(self.path_model      )
													SCREAMING_SNAKE_CASE__     :Any						    = GPTaTokenizer.from_pretrained(self.path_model      )

													SCREAMING_SNAKE_CASE__     :Any						    = [
													    'Today is a beautiful day and I want to',
													    'In the city of',
													    'Paris is the capital of France and',
													    'Computers and mobile phones have taken',
													]
													# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
													SCREAMING_SNAKE_CASE__     :Dict						    = tokenizer(UpperCamelCase_							,							return_tensors='tf'							,							padding=UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = tf.math.reduce_mean(model(inputs.input_ids							,							attention_mask=inputs.attention_mask      )[0]							,							axis=-1      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tf.constant(
													    [
													        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
													        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
													        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
													        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
													    ]      )
													self.assertTrue(np.allclose(UpperCamelCase_							,							UpperCamelCase_							,							atol=1e-4      )      )

													SCREAMING_SNAKE_CASE__     :Dict						    = tf.function(UpperCamelCase_							,							jit_compile=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = tf.math.reduce_mean(xla_generate(inputs.input_ids							,							attention_mask=inputs.attention_mask      )[0]							,							axis=-1      )
													self.assertTrue(np.allclose(UpperCamelCase_							,							UpperCamelCase_							,							atol=1e-4      )      )




@require_tf
@slow
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						@property
						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													return [
													    "Today is a beautiful day and I want",
													    "In the city of",
													    "Paris is the capital of France and",
													    "Computers and mobile phones have taken",
													]




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'facebook/opt-125m'

													SCREAMING_SNAKE_CASE__     :List[str]						    = [
													    'Today is a beautiful day and I want to',
													    'In the city of New York, the city',
													    'Paris is the capital of France and the capital',
													    'Computers and mobile phones have taken over the',
													]

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = []
													SCREAMING_SNAKE_CASE__     :Tuple						    = GPTaTokenizer.from_pretrained(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = TFOPTForCausalLM.from_pretrained(UpperCamelCase_      )

													for prompt in self.prompts:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer(UpperCamelCase_							,							return_tensors='tf'      ).input_ids

																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = model.generate(UpperCamelCase_							,							max_length=10      )

																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer.batch_decode(UpperCamelCase_							,							skip_special_tokens=UpperCamelCase_      )
																				predicted_outputs += generated_string

													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Tuple						    = 'facebook/opt-350m'

													SCREAMING_SNAKE_CASE__     :Any						    = GPTaTokenizer.from_pretrained(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = TFOPTForCausalLM.from_pretrained(UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Dict						    = 'left'

													# use different length sentences to test batching
													SCREAMING_SNAKE_CASE__     :List[str]						    = [
													    'Hello, my dog is a little',
													    'Today, I',
													]

													SCREAMING_SNAKE_CASE__     :List[str]						    = tokenizer(UpperCamelCase_							,							return_tensors='tf'							,							padding=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = inputs['input_ids']

													SCREAMING_SNAKE_CASE__     :str						    = model.generate(input_ids=UpperCamelCase_							,							attention_mask=inputs['attention_mask']      )

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tokenizer(sentences[0]							,							return_tensors='tf'      ).input_ids
													SCREAMING_SNAKE_CASE__     :Any						    = model.generate(input_ids=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
													    tf.cast(inputs['attention_mask'][-1]							,							tf.intaa      )      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer(sentences[1]							,							return_tensors='tf'      ).input_ids
													SCREAMING_SNAKE_CASE__     :Dict						    = model.generate(input_ids=UpperCamelCase_							,							max_length=model.config.max_length - num_paddings      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer.batch_decode(UpperCamelCase_							,							skip_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer.decode(output_non_padded[0]							,							skip_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = tokenizer.decode(output_padded[0]							,							skip_special_tokens=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :str						    = [
													    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
													    'Today, I was in the middle of a conversation with a friend about the',
													]
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							[non_padded_sentence, padded_sentence]      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[str]						    = 'facebook/opt-350m'

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = [
													    'Today is a beautiful day and I want to',
													    'In the city of San Francisco, the city',
													    'Paris is the capital of France and the capital',
													    'Computers and mobile phones have taken over the',
													]

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = []
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = GPTaTokenizer.from_pretrained(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = TFOPTForCausalLM.from_pretrained(UpperCamelCase_      )

													for prompt in self.prompts:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = tokenizer(UpperCamelCase_							,							return_tensors='tf'      ).input_ids

																				SCREAMING_SNAKE_CASE__     :int						    = model.generate(UpperCamelCase_							,							max_length=10      )

																				SCREAMING_SNAKE_CASE__     :str						    = tokenizer.batch_decode(UpperCamelCase_							,							skip_special_tokens=UpperCamelCase_      )
																				predicted_outputs += generated_string

													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )







'''simple docstring'''







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    lowerCamelCase       (			UpperCAmelCase__				:  Union[dict, list, tuple, torch.Tensor]		)			->	List[Tuple[int, ...]]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[int]						    = []
							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    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  Tuple[int, ...]		)			->	Tuple[int, ...]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Dict						    = []
							for d in reversed(UpperCAmelCase__		):
														idx.append(flat_idx % d		)
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = flat_idx // d

							return tuple(reversed(UpperCAmelCase__		)		)


@torch.jit.ignore
def    lowerCamelCase       (			UpperCAmelCase__				:  Sequence[int]				,						UpperCAmelCase__				:  Sequence[int]				,						UpperCAmelCase__				:  Sequence[int]				,						UpperCAmelCase__				:  Optional[Sequence[bool]] = None				,						UpperCAmelCase__				:  Optional[Sequence[bool]] = None				,						)			->	List[Tuple[slice, ...]]:


							'''simple docstring'''






							def reduce_edge_list(UpperCAmelCase__				:  List[bool]		) -> None:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = True
														for i in range(len(UpperCAmelCase__		)		):
																					SCREAMING_SNAKE_CASE__     :List[Any]						    = -1 * (i + 1)
																					l[reversed_idx] &= tally
																					SCREAMING_SNAKE_CASE__     :Tuple						    = l[reversed_idx]

							if start_edges is None:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = [s == 0 for s in start]
														reduce_edge_list(UpperCAmelCase__		)
							if end_edges is None:
														SCREAMING_SNAKE_CASE__     :Dict						    = [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__     :List[Tuple[slice, ...]]						    = []
							SCREAMING_SNAKE_CASE__     :List[slice]						    = []

							# 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[slice, ...]						    = tuple(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 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__     :Optional[Any]						    = 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__     :Any						    = 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__     :str						    = 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    lowerCamelCase       (			UpperCAmelCase__				:  torch.Tensor				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int		)			->	torch.Tensor:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = t.shape[:no_batch_dims]
							SCREAMING_SNAKE_CASE__     :int						    = list(_flat_idx_to_idx(UpperCAmelCase__				,						UpperCAmelCase__		)		)
							# _get_minimal_slice_set is inclusive
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = list(_flat_idx_to_idx(flat_end - 1				,						UpperCAmelCase__		)		)

							# Get an ordered list of slices to perform
							SCREAMING_SNAKE_CASE__     :List[Any]						    = _get_minimal_slice_set(
							    UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						)

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

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


def    lowerCamelCase       (			UpperCAmelCase__				:  Callable				,						UpperCAmelCase__				:  Dict[str, Any]				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  bool = False				,						UpperCAmelCase__				:  Any = None				,						UpperCAmelCase__				:  bool = False				,						)			->	Any:


							'''simple docstring'''






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

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [shape[:no_batch_dims] for shape in _fetch_dims(UpperCAmelCase__		)]
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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__     :Tuple						    = t.expand(orig_batch_dims + t.shape[no_batch_dims:]		)
																					SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = t.reshape(-1				,						*t.shape[no_batch_dims:]		)
														else:
																					SCREAMING_SNAKE_CASE__     :Any						    = t.expand(orig_batch_dims + t.shape[no_batch_dims:]		)
														return t

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

							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 1
							for d in orig_batch_dims:
														flat_batch_dim *= d

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

														SCREAMING_SNAKE_CASE__     :Dict[str, Any]						    = tensor_tree_map(UpperCAmelCase__				,						UpperCAmelCase__		)

														# Run the layer on the chunk
														SCREAMING_SNAKE_CASE__     :Any						    = layer(**UpperCAmelCase__		)

														# Allocate space for the output
														if out is None:
																					SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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__     :str						    = 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__     :int						    = xa
														elif isinstance(UpperCAmelCase__				,						torch.Tensor		):
																					if _add_into_out:
																												out[i : i + chunk_size] += output_chunk
																					else:
																												SCREAMING_SNAKE_CASE__     :List[Any]						    = output_chunk
														else:
																					raise ValueError('Not supported'		)

														i += chunk_size

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

							return out





class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       Any							,							UpperCamelCase_ :       int = 5_12							,							)     ->				int:
													SCREAMING_SNAKE_CASE__     :int						    = max_chunk_size
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = None
													SCREAMING_SNAKE_CASE__     :Optional[tuple]						    = None




						def      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       Callable							,							UpperCamelCase_ :       tuple							,							UpperCamelCase_ :       int      )     ->				int:
													logging.info('Tuning chunk size...'      )

													if min_chunk_size >= self.max_chunk_size:
																				return min_chunk_size

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

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

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 0
													SCREAMING_SNAKE_CASE__     :List[str]						    = len(UpperCamelCase_      ) - 1
													while i > min_viable_chunk_size_index:
																				SCREAMING_SNAKE_CASE__     :int						    = test_chunk_size(candidates[i]      )
																				if not viable:
																											SCREAMING_SNAKE_CASE__     :List[Any]						    = (min_viable_chunk_size_index + i) // 2
																				else:
																											SCREAMING_SNAKE_CASE__     :int						    = i
																											SCREAMING_SNAKE_CASE__     :List[str]						    = (i + len(UpperCamelCase_      ) - 1) // 2

													return candidates[min_viable_chunk_size_index]




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

													if not consistent:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = self._determine_favorable_chunk_size(
																				    UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							)
																				SCREAMING_SNAKE_CASE__     :int						    = arg_data

													assert self.cached_chunk_size is not None

													return self.cached_chunk_size






