






"""simple docstring"""



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class        lowercase__      :


			def __init__(				self					:  Any       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  Any=99       ,	snake_case__					:  Any=13       ,	snake_case__					:  str=7       ,	snake_case__					:  List[str]=9       ,	snake_case__					:  str=True       ,	snake_case__					:  Any=True       ,	snake_case__					:  Optional[Any]=False       ,	snake_case__					:  Optional[Any]=32       ,	snake_case__					:  Any=5       ,	snake_case__					:  Any=4       ,	snake_case__					:  List[str]=37       ,	snake_case__					:  Tuple=8       ,	snake_case__					:  Dict=0.1       ,	snake_case__					:  Optional[Any]=0.002       ,	snake_case__					:  Tuple=1       ,	snake_case__					:  Union[str, Any]=0       ,	snake_case__					:  Any=0       ,	snake_case__					:  Union[str, Any]=None       ,	snake_case__					:  List[Any]=None       ,	):
										lowerCamelCase_	:							str									=parent
										lowerCamelCase_	:							List[str]									=batch_size
										lowerCamelCase_	:							Any									=encoder_seq_length
										lowerCamelCase_	:							Optional[Any]									=decoder_seq_length
										# For common tests
										lowerCamelCase_	:							Tuple									=self.decoder_seq_length
										lowerCamelCase_	:							str									=is_training
										lowerCamelCase_	:							List[str]									=use_attention_mask
										lowerCamelCase_	:							Tuple									=use_labels
										lowerCamelCase_	:							Optional[Any]									=vocab_size
										lowerCamelCase_	:							Dict									=hidden_size
										lowerCamelCase_	:							Any									=num_hidden_layers
										lowerCamelCase_	:							Union[str, Any]									=num_attention_heads
										lowerCamelCase_	:							List[str]									=d_ff
										lowerCamelCase_	:							Optional[Any]									=relative_attention_num_buckets
										lowerCamelCase_	:							List[str]									=dropout_rate
										lowerCamelCase_	:							Union[str, Any]									=initializer_factor
										lowerCamelCase_	:							Optional[int]									=eos_token_id
										lowerCamelCase_	:							List[str]									=pad_token_id
										lowerCamelCase_	:							Dict									=decoder_start_token_id
										lowerCamelCase_	:							Tuple									=None
										lowerCamelCase_	:							Optional[int]									=decoder_layers


			def      UpperCAmelCase__						(				self					:  List[str] ):
										return TaConfig.from_pretrained("google/umt5-base" )


			def      UpperCAmelCase__						(				self					:  str       ,	snake_case__					:  List[Any]       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  List[Any]       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  Dict=None       ,	snake_case__					:  Union[str, Any]=None       ,	snake_case__					:  List[str]=None       ,	snake_case__					:  int=None       ,	):
										if attention_mask is None:
																	lowerCamelCase_	:							int									=input_ids.ne(config.pad_token_id )
										if decoder_attention_mask is None:
																	lowerCamelCase_	:							Union[str, Any]									=decoder_input_ids.ne(config.pad_token_id )
										if head_mask is None:
																	lowerCamelCase_	:							Dict									=torch.ones(config.num_hidden_layers       ,	config.num_attention_heads       ,	device=snake_case__ )
										if decoder_head_mask is None:
																	lowerCamelCase_	:							Optional[Any]									=torch.ones(config.num_decoder_layers       ,	config.num_attention_heads       ,	device=snake_case__ )
										if cross_attn_head_mask is None:
																	lowerCamelCase_	:							Optional[Any]									=torch.ones(
																	    config.num_decoder_layers       ,	config.num_attention_heads       ,	device=snake_case__ )
										return {
										    "input_ids": input_ids,
										    "decoder_input_ids": decoder_input_ids,
										    "attention_mask": attention_mask,
										    "decoder_attention_mask": decoder_attention_mask,
										    "head_mask": head_mask,
										    "decoder_head_mask": decoder_head_mask,
										    "cross_attn_head_mask": cross_attn_head_mask,
										}


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							Dict									=ids_tensor([self.batch_size, self.encoder_seq_length]       ,	self.vocab_size )
										lowerCamelCase_	:							Dict									=ids_tensor([self.batch_size, self.decoder_seq_length]       ,	self.vocab_size )

										# we need to clamp the input ids here to avoid having pad token in between
										# this is because for NllbMoe the position_ids are prepared such that
										# all pad tokens have pos id = 2 and rest are between 2..seq_length
										# and the seq_length here is seq_length - num_pad_tokens
										# but when using past, there is no way of knowing if the past input ids had
										# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
										# position_ids being off by num_pad_tokens in past input
										lowerCamelCase_	:							Union[str, Any]									=input_ids.clamp(self.pad_token_id + 1 )
										lowerCamelCase_	:							List[str]									=decoder_input_ids.clamp(self.pad_token_id + 1 )

										lowerCamelCase_	:							str									=self.get_config()
										lowerCamelCase_	:							Union[str, Any]									=config.num_attention_heads
										lowerCamelCase_	:							List[str]									=self.prepare_inputs_dict(snake_case__       ,	snake_case__       ,	snake_case__ )
										return config, input_dict


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=self.prepare_config_and_inputs()
										return config, inputs_dict


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return TaConfig(
										    vocab_size=166       ,	d_model=self.hidden_size       ,	d_ff=self.d_ff       ,	d_kv=self.hidden_size // self.num_attention_heads       ,	num_layers=self.num_hidden_layers       ,	num_decoder_layers=self.decoder_layers       ,	num_heads=self.num_attention_heads       ,	relative_attention_num_buckets=self.relative_attention_num_buckets       ,	dropout_rate=self.dropout_rate       ,	initializer_factor=self.initializer_factor       ,	eos_token_id=self.eos_token_id       ,	bos_token_id=self.pad_token_id       ,	pad_token_id=self.pad_token_id       ,	decoder_start_token_id=self.decoder_start_token_id       ,	)


			def      UpperCAmelCase__						(				self					:  str ):
										return TaConfig(
										    vocab_size=self.vocab_size       ,	d_model=self.hidden_size       ,	d_ff=self.d_ff       ,	d_kv=self.hidden_size // self.num_attention_heads       ,	num_layers=self.num_hidden_layers       ,	num_decoder_layers=self.decoder_layers       ,	num_heads=self.num_attention_heads       ,	relative_attention_num_buckets=self.relative_attention_num_buckets       ,	dropout_rate=self.dropout_rate       ,	initializer_factor=self.initializer_factor       ,	eos_token_id=self.eos_token_id       ,	bos_token_id=self.pad_token_id       ,	pad_token_id=self.pad_token_id       ,	decoder_start_token_id=self.decoder_start_token_id       ,	)


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  Tuple       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  str       ,	snake_case__					:  Tuple       ,	snake_case__					:  str       ,	snake_case__					:  Union[str, Any]       ,	):
										lowerCamelCase_	:							List[Any]									=UMTaModel(config=snake_case__ )
										model.to(snake_case__ )
										model.eval()
										lowerCamelCase_	:							Any									=model(
										    input_ids=snake_case__       ,	decoder_input_ids=snake_case__       ,	attention_mask=snake_case__       ,	decoder_attention_mask=snake_case__       ,	)
										lowerCamelCase_	:							str									=model(input_ids=snake_case__       ,	decoder_input_ids=snake_case__ )
										lowerCamelCase_	:							List[Any]									=result.last_hidden_state
										lowerCamelCase_	:							Dict									=result.past_key_values
										lowerCamelCase_	:							Dict									=result.encoder_last_hidden_state

										self.parent.assertEqual(encoder_output.size()       ,	(self.batch_size, self.encoder_seq_length, self.hidden_size) )
										self.parent.assertEqual(decoder_output.size()       ,	(self.batch_size, self.decoder_seq_length, self.hidden_size) )
										# There should be `num_layers` key value embeddings stored in decoder_past
										self.parent.assertEqual(len(snake_case__ )       ,	config.num_layers )
										# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
										self.parent.assertEqual(len(decoder_past[0] )       ,	4 )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  int       ,	snake_case__					:  List[str]       ,	snake_case__					:  Tuple       ,	snake_case__					:  str       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  Union[str, Any]       ,	):
										lowerCamelCase_	:							List[Any]									=UMTaModel(config=snake_case__ ).get_decoder().to(snake_case__ ).eval()
										# first forward pass
										lowerCamelCase_	:							List[Any]									=model(snake_case__       ,	use_cache=snake_case__ )
										lowerCamelCase_	:							Dict									=model(snake_case__ )
										lowerCamelCase_	:							int									=model(snake_case__       ,	use_cache=snake_case__ )

										self.parent.assertTrue(len(snake_case__ ) == len(snake_case__ ) )
										self.parent.assertTrue(len(snake_case__ ) == len(snake_case__ ) + 1 )

										lowerCamelCase_							,						lowerCamelCase_	:							Optional[Any]									=outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										lowerCamelCase_	:							str									=ids_tensor((self.batch_size, 1)       ,	config.vocab_size )

										# append to next input_ids and
										lowerCamelCase_	:							List[Any]									=torch.cat([input_ids, next_tokens]       ,	dim=-1 )

										lowerCamelCase_	:							Optional[int]									=model(snake_case__ )["last_hidden_state"]
										lowerCamelCase_	:							Tuple									=model(snake_case__       ,	past_key_values=snake_case__ )["last_hidden_state"]

										# select random slice
										lowerCamelCase_	:							int									=ids_tensor((1,)       ,	output_from_past.shape[-1] ).item()
										lowerCamelCase_	:							Union[str, Any]									=output_from_no_past[:, -1, random_slice_idx].detach()
										lowerCamelCase_	:							Union[str, Any]									=output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(snake_case__       ,	snake_case__       ,	atol=1E-3 ) )


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  Dict       ,	snake_case__					:  List[Any]       ,	):
										lowerCamelCase_	:							Tuple									=UMTaModel(config=snake_case__ ).to(snake_case__ ).half().eval()
										lowerCamelCase_	:							Dict									=model(**snake_case__ )["last_hidden_state"]
										self.parent.assertFalse(torch.isnan(snake_case__ ).any().item() )







@require_torch
class        lowercase__      (							snake_case__,       snake_case__,       snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Optional[Any]             =			(
			    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
			)
			_UpperCAmelCase		:Dict             =			(UMTaForConditionalGeneration,) if is_torch_available() else ()
			_UpperCAmelCase		:Any             =			(
			    {
			        "conversational": UMTaForConditionalGeneration,
			        "feature-extraction": UMTaModel,
			        "summarization": UMTaForConditionalGeneration,
			        "text2text-generation": UMTaForConditionalGeneration,
			        "translation": UMTaForConditionalGeneration,
			        "question-answering": UMTaForQuestionAnswering,
			    }
			    if is_torch_available()
			    else {}
			)
			_UpperCAmelCase		:int             =			True
			_UpperCAmelCase		:Union[str, Any]             =			False
			_UpperCAmelCase		:Union[str, Any]             =			False
			_UpperCAmelCase		:Tuple             =			True
			_UpperCAmelCase		:str             =			True
			# The small UMT5 model needs higher percentages for CPU/MP tests
			_UpperCAmelCase		:int             =			[0.8, 0.9]


			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							Any									=UMTaModelTester(self )


			@unittest.skip("Test has a segmentation fault on torch 1.8.0" )
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							Dict									=self.model_tester.prepare_config_and_inputs()
										lowerCamelCase_	:							Optional[int]									=UMTaModel(config_and_inputs[0] ).to(snake_case__ )
										with tempfile.TemporaryDirectory() as tmpdirname:
																	torch.onnx.export(
																	    snake_case__       ,	(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])       ,	F"""{tmpdirname}/t5_test.onnx"""       ,	export_params=snake_case__       ,	opset_version=9       ,	input_names=["input_ids", "decoder_input_ids"]       ,	)


			@unittest.skipIf(torch_device == "cpu"       ,	"Cant do half precision" )
			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							Union[str, Any]									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model_fpaa_forward(*snake_case__ )


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							List[str]									=["encoder_attentions", "decoder_attentions", "cross_attentions"]
										lowerCamelCase_	:							Any									=self.model_tester.prepare_config_and_inputs()
										lowerCamelCase_	:							Union[str, Any]									=config_and_inputs[0]
										lowerCamelCase_	:							Dict									=UMTaForConditionalGeneration(snake_case__ ).eval()
										model.to(snake_case__ )

										lowerCamelCase_	:							List[Any]									={
										    "head_mask": torch.zeros(config.num_layers       ,	config.num_heads       ,	device=snake_case__ ),
										    "decoder_head_mask": torch.zeros(config.num_decoder_layers       ,	config.num_heads       ,	device=snake_case__ ),
										    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers       ,	config.num_heads       ,	device=snake_case__ ),
										}

										for attn_name, (name, mask) in zip(snake_case__       ,	head_masking.items() ):
																	lowerCamelCase_	:							str									={name: mask}
																	# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																	if name == "head_mask":
																								lowerCamelCase_	:							Dict									=torch.ones(
																								    config.num_decoder_layers       ,	config.num_heads       ,	device=snake_case__ )

																	lowerCamelCase_	:							List[Any]									=model.generate(
																	    config_and_inputs[1]["input_ids"]       ,	num_beams=1       ,	max_length=3       ,	output_attentions=snake_case__       ,	return_dict_in_generate=snake_case__       ,	**snake_case__       ,	)
																	# We check the state of decoder_attentions and cross_attentions just from the last step
																	lowerCamelCase_	:							Optional[Any]									=out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
																	self.assertEqual(sum([w.sum().item() for w in attn_weights] )       ,	0.0 )


			@unittest.skip("Does not work on the tiny model as we keep hitting edge cases." )
			def      UpperCAmelCase__						(				self					:  Tuple ):
										pass







@require_torch
@require_sentencepiece
@require_tokenizers
class        lowercase__      (							unittest.TestCase       ):


			@slow
			@unittest.skip(
			    "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged" )
			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							Dict									=UMTaForConditionalGeneration.from_pretrained("google/umt5-small"       ,	return_dict=snake_case__ ).to(snake_case__ )
										lowerCamelCase_	:							Any									=AutoTokenizer.from_pretrained("google/umt5-small"       ,	use_fast=snake_case__       ,	legacy=snake_case__ )
										lowerCamelCase_	:							str									=[
										    "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
										    "No se como puedo <extra_id_0>.",
										    "This is the reason why we <extra_id_0> them.",
										    "The <extra_id_0> walks in <extra_id_1>, seats",
										    "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
										]
										lowerCamelCase_	:							Tuple									=tokenizer(snake_case__       ,	return_tensors="pt"       ,	padding=snake_case__ ).input_ids
										# fmt: off
										lowerCamelCase_	:							Dict									=torch.tensor(
										    [
										        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
										        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
										    ] )
										# fmt: on
										torch.testing.assert_allclose(snake_case__       ,	snake_case__ )

										lowerCamelCase_	:							Optional[int]									=model.generate(input_ids.to(snake_case__ ) )
										lowerCamelCase_	:							Any									=[
										    "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
										    "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
										    "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
										    "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
										    "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
										]
										lowerCamelCase_	:							str									=tokenizer.batch_decode(snake_case__ )
										self.assertEqual(snake_case__       ,	snake_case__ )











"""simple docstring"""



# 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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser

def 			_snake_case    (					)    ->     Tuple:
							lowerCamelCase_	:							Optional[int]									=ArgumentParser("Accelerate CLI tool"    ,						usage="accelerate <command> [<args>]"    ,						allow_abbrev=lowerCamelCase__  )
							lowerCamelCase_	:							Optional[int]									=parser.add_subparsers(help="accelerate command helpers"  )

							# Register commands
							get_config_parser(subparsers=lowerCamelCase__  )
							env_command_parser(subparsers=lowerCamelCase__  )
							launch_command_parser(subparsers=lowerCamelCase__  )
							tpu_command_parser(subparsers=lowerCamelCase__  )
							test_command_parser(subparsers=lowerCamelCase__  )

							# Let's go
							lowerCamelCase_	:							int									=parser.parse_args()

							if not hasattr(lowerCamelCase__    ,						"func"  ):
														parser.print_help()
														exit(1  )

							# Run
							args.func(lowerCamelCase__  )


if __name__ == "__main__":
						main()



