'''simple docstring'''




import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_lowercase		:				str	       =		logging.getLogger()

_lowercase		:				Optional[int]	       =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 					__magic_name__		(					_UpperCAmelCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						Any					):
										os.makedirs(lowercase_       ,    exist_ok=lowercase_					)
										lowercase_      :    str       		= {"""source""": """What is love ?""", """target""": """life"""}
										lowercase_      :    List[str]       		= {"""train""": 12, """val""": 2, """test""": 2}
										for split in ["train", "test", "val"]:
																	for field in ["source", "target"]:
																								lowercase_      :    Optional[Any]       		= """\n""".join([contents[field]] * n_lines[split]					)
																								with open(os.path.join(lowercase_       ,    f'''{split}.{field}'''					)       ,    """w"""					) as f:
																															f.write(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						int       ,    lowercase_       :						str = "pytorch"					):
										lowercase_      :    Optional[Any]       		= self.get_auto_remove_tmp_dir()
										lowercase_      :    List[Any]       		= os.path.join(lowercase_       ,    """output"""					)
										lowercase_      :    Any       		= os.path.join(lowercase_       ,    """data"""					)
										self._create_dummy_data(data_dir=lowercase_					)

										lowercase_      :    Dict       		= f'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

										if gpus > 0:
																	testargs.append(f'''--gpus={gpus}'''					)
																	if is_apex_available():
																								testargs.append("""--fp16"""					)
										else:
																	testargs.append("""--gpus=0"""					)
																	testargs.append("""--distributed_backend=ddp_cpu"""					)
																	testargs.append("""--num_processes=2"""					)

										lowercase_      :    Any       		= [sys.executable, str(Path(finetune_rag.__file__					).resolve()					)] + testargs
										execute_subprocess_async(lowercase_       ,    env=self.get_env()					)

										lowercase_      :    List[str]       		= os.path.join(lowercase_       ,    """metrics.json"""					)
										with open(lowercase_					) as f:
																	lowercase_      :    Optional[int]       		= json.load(lowercase_					)
										return result







			@require_torch_gpu
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_      :    str       		= self._run_finetune(gpus=1					)
										self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,    0.2					)







			@require_torch_multi_gpu
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    Union[str, Any]       		= self._run_finetune(gpus=2					)
										self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,    0.2					)







			@require_torch_gpu
			@require_ray
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    Union[str, Any]       		= self._run_finetune(gpus=1       ,    distributed_retriever="""ray"""					)
										self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,    0.2					)







			@require_torch_multi_gpu
			@require_ray
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    Dict       		= self._run_finetune(gpus=1       ,    distributed_retriever="""ray"""					)
										self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,    0.2					)






'''simple docstring'''




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 					__magic_name__		(					unittest.TestCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    List[Any]       		= """ylacombe/bark-small"""
										lowercase_      :    List[str]       		= tempfile.mkdtemp()
										lowercase_      :    Tuple       		= """en_speaker_1"""
										lowercase_      :    Union[str, Any]       		= """This is a test string"""
										lowercase_      :    int       		= """speaker_embeddings_path.json"""
										lowercase_      :    Any       		= """speaker_embeddings"""







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    **lowercase_       :						Optional[int]					):
										return AutoTokenizer.from_pretrained(self.checkpoint       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										shutil.rmtree(self.tmpdirname					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Any       		= self.get_tokenizer()

										lowercase_      :    Optional[Any]       		= BarkProcessor(tokenizer=lowercase_					)

										processor.save_pretrained(self.tmpdirname					)
										lowercase_      :    Union[str, Any]       		= BarkProcessor.from_pretrained(self.tmpdirname					)

										self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer.get_vocab()					)







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Any       		= BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    )
										processor.save_pretrained(
										    self.tmpdirname       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    speaker_embeddings_directory=self.speaker_embeddings_directory       ,    )

										lowercase_      :    List[str]       		= self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""					)

										lowercase_      :    Optional[Any]       		= BarkProcessor.from_pretrained(
										    self.tmpdirname       ,    self.speaker_embeddings_dict_path       ,    bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""       ,    )

										self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab()					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    )

										lowercase_      :    Optional[int]       		= 35
										lowercase_      :    int       		= 2
										lowercase_      :    Union[str, Any]       		= 8

										lowercase_      :    Union[str, Any]       		= {
										    """semantic_prompt""": np.ones(lowercase_					),
										    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)					),
										    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)					),
										}

										# test providing already loaded voice_preset
										lowercase_      :    str       		= processor(text=self.input_string       ,    voice_preset=lowercase_					)

										lowercase_      :    Dict       		= inputs["""history_prompt"""]
										for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()       ,    processed_voice_preset.get(lowercase_       ,    np.array([]					)					).tolist()					)

										# test loading voice preset from npz file
										lowercase_      :    Any       		= os.path.join(self.tmpdirname       ,    """file.npz"""					)
										np.savez(lowercase_       ,    **lowercase_					)
										lowercase_      :    Optional[Any]       		= processor(text=self.input_string       ,    voice_preset=lowercase_					)
										lowercase_      :    List[Any]       		= inputs["""history_prompt"""]

										for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()       ,    processed_voice_preset.get(lowercase_       ,    np.array([]					)					).tolist()					)

										# test loading voice preset from the hub
										lowercase_      :    Union[str, Any]       		= processor(text=self.input_string       ,    voice_preset=self.voice_preset					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    List[str]       		= self.get_tokenizer()

										lowercase_      :    int       		= BarkProcessor(tokenizer=lowercase_					)

										lowercase_      :    Any       		= processor(text=self.input_string					)

										lowercase_      :    List[str]       		= tokenizer(
										    self.input_string       ,    padding="""max_length"""       ,    max_length=256       ,    add_special_tokens=lowercase_       ,    return_attention_mask=lowercase_       ,    return_token_type_ids=lowercase_       ,    )

										for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key].squeeze().tolist()					)





