





import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCamelCase__      =[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
       import run_clm_flax
       import run_flax_glue
       import run_flax_ner
       import run_mlm_flax
       import run_qa
       import run_summarization_flax
       import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase__      =logging.getLogger()






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	Any						    =							argparse.ArgumentParser()
     parser.add_argument("-f"	)
     _SCREAMING_SNAKE_CASE       :	str						    =							parser.parse_args()
     return args.f






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase="eval"	):
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							os.path.join(__lowerCamelCase,       f"""{split}_results.json"""	)
     if os.path.exists(__lowerCamelCase	):
          with open(__lowerCamelCase,       "r"	) as f:
               return json.load(__lowerCamelCase	)
     raise ValueError(f"""can't find {path}"""	)


UpperCamelCase__      =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

            with patch.object(__lowerCamelCase    ,						"argv"    ,						__lowerCamelCase							):
                 run_flax_glue.main()
                 _SCREAMING_SNAKE_CASE       :	Any						    =							get_results(__lowerCamelCase							)
                 self.assertGreaterEqual(result["eval_accuracy"]    ,						0.75							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

            with patch.object(__lowerCamelCase    ,						"argv"    ,						__lowerCamelCase							):
                 run_clm_flax.main()
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							get_results(__lowerCamelCase							)
                 self.assertLess(result["eval_perplexity"]    ,						1_0_0							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

            with patch.object(__lowerCamelCase    ,						"argv"    ,						__lowerCamelCase							):
                 run_summarization_flax.main()
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							get_results(__lowerCamelCase    ,						split="test"							)
                 self.assertGreaterEqual(result["test_rouge1"]    ,						1_0							)
                 self.assertGreaterEqual(result["test_rouge2"]    ,						2							)
                 self.assertGreaterEqual(result["test_rougeL"]    ,						7							)
                 self.assertGreaterEqual(result["test_rougeLsum"]    ,						7							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE       :	int						    =							F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

            with patch.object(__lowerCamelCase    ,						"argv"    ,						__lowerCamelCase							):
                 run_mlm_flax.main()
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							get_results(__lowerCamelCase							)
                 self.assertLess(result["eval_perplexity"]    ,						4_2							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE       :	Any						    =							F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

            with patch.object(__lowerCamelCase    ,						"argv"    ,						__lowerCamelCase							):
                 run_ta_mlm_flax.main()
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							get_results(__lowerCamelCase							)
                 self.assertGreaterEqual(result["eval_accuracy"]    ,						0.42							)




       @slow
       def     UpperCamelCase_			(      self							)			->     List[Any]:
            # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
            _SCREAMING_SNAKE_CASE       :	Any						    =							7 if get_gpu_count() > 1 else 2

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

            with patch.object(__lowerCamelCase    ,						"argv"    ,						__lowerCamelCase							):
                 run_flax_ner.main()
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							get_results(__lowerCamelCase							)
                 self.assertGreaterEqual(result["eval_accuracy"]    ,						0.75							)
                 self.assertGreaterEqual(result["eval_f1"]    ,						0.3							)







       @slow
       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

            with patch.object(__lowerCamelCase    ,						"argv"    ,						__lowerCamelCase							):
                 run_qa.main()
                 _SCREAMING_SNAKE_CASE       :	Any						    =							get_results(__lowerCamelCase							)
                 self.assertGreaterEqual(result["eval_f1"]    ,						3_0							)
                 self.assertGreaterEqual(result["eval_exact"]    ,						3_0							)






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    42





class 			lowerCAmelCase__( __lowercase  ,     __lowercase    ):






       '''simple docstring'''


       @register_to_config
       def __init__(      self    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = ("DownEncoderBlock2D",)    ,						__lowerCamelCase = ("UpDecoderBlock2D",)    ,						__lowerCamelCase = (6_4,)    ,						__lowerCamelCase = 1    ,						__lowerCamelCase = "silu"    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = 2_5_6    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = None    ,						__lowerCamelCase = 0.1_8215    ,						__lowerCamelCase = "group"    ,						)			->     List[str]:
            super().__init__()

            # pass init params to Encoder
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							Encoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						down_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						double_z=__lowerCamelCase    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							vq_embed_dim if vq_embed_dim is not None else latent_channels

            _SCREAMING_SNAKE_CASE       :	str						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							VectorQuantizer(__lowerCamelCase    ,						__lowerCamelCase    ,						beta=0.25    ,						remap=__lowerCamelCase    ,						sane_index_shape=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)

            # pass init params to Decoder
            _SCREAMING_SNAKE_CASE       :	Dict						    =							Decoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						up_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						norm_type=__lowerCamelCase    ,						)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     VQEncoderOutput:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.encoder(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.quant_conv(__lowerCamelCase							)

            if not return_dict:
                 return (h,)

            return VQEncoderOutput(latents=__lowerCamelCase							)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = False    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.quantize(__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							h
            _SCREAMING_SNAKE_CASE       :	str						    =							self.post_quant_conv(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.decoder(__lowerCamelCase    ,						quant if self.config.norm_type == "spatial" else None							)

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							sample
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.encode(__lowerCamelCase							).latents
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.decode(__lowerCamelCase							).sample

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)