



"""simple docstring"""


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


__A   :							Optional[int]										=   [
    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)

__A   :							int										=   logging.getLogger()







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    argparse.ArgumentParser()
				parser.add_argument('''-f'''				)
				_UpperCAmelCase					=    parser.parse_args()
				return args.f







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						str="eval"				):






				'''simple docstring'''



				_UpperCAmelCase					=    os.path.join(_SCREAMING_SNAKE_CASE					,     f'{split}_results.json'				)
				if os.path.exists(_SCREAMING_SNAKE_CASE				):
								with open(_SCREAMING_SNAKE_CASE					,     '''r'''				) as f:
												return json.load(_SCREAMING_SNAKE_CASE				)
				raise ValueError(f'can\'t find {path}'				)


__A   :							Optional[Any]										=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



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

							with patch.object(__UpperCamelCase				,   '''argv'''				,   __UpperCamelCase   ):
											run_flax_glue.main()
											_UpperCAmelCase					=    get_results(__UpperCamelCase   )
											self.assertGreaterEqual(result['''eval_accuracy''']				,   0.7_5   )



			@slow
			def  lowercase__						(		self     :    Union[str, Any]   )->Union[str, Any]:
							_UpperCAmelCase					=    self.get_auto_remove_tmp_dir()
							_UpperCAmelCase					=    F'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

							with patch.object(__UpperCamelCase				,   '''argv'''				,   __UpperCamelCase   ):
											run_clm_flax.main()
											_UpperCAmelCase					=    get_results(__UpperCamelCase   )
											self.assertLess(result['''eval_perplexity''']				,   1_0_0   )



			@slow
			def  lowercase__						(		self     :    Optional[int]   )->Optional[int]:
							_UpperCAmelCase					=    self.get_auto_remove_tmp_dir()
							_UpperCAmelCase					=    F'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

							with patch.object(__UpperCamelCase				,   '''argv'''				,   __UpperCamelCase   ):
											run_summarization_flax.main()
											_UpperCAmelCase					=    get_results(__UpperCamelCase				,   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  lowercase__						(		self     :    List[str]   )->str:
							_UpperCAmelCase					=    self.get_auto_remove_tmp_dir()
							_UpperCAmelCase					=    F'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

							with patch.object(__UpperCamelCase				,   '''argv'''				,   __UpperCamelCase   ):
											run_mlm_flax.main()
											_UpperCAmelCase					=    get_results(__UpperCamelCase   )
											self.assertLess(result['''eval_perplexity''']				,   4_2   )



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

							with patch.object(__UpperCamelCase				,   '''argv'''				,   __UpperCamelCase   ):
											run_ta_mlm_flax.main()
											_UpperCAmelCase					=    get_results(__UpperCamelCase   )
											self.assertGreaterEqual(result['''eval_accuracy''']				,   0.4_2   )



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

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

							with patch.object(__UpperCamelCase				,   '''argv'''				,   __UpperCamelCase   ):
											run_flax_ner.main()
											_UpperCAmelCase					=    get_results(__UpperCamelCase   )
											self.assertGreaterEqual(result['''eval_accuracy''']				,   0.7_5   )
											self.assertGreaterEqual(result['''eval_f1''']				,   0.3   )



			@slow
			def  lowercase__						(		self     :    Union[str, Any]   )->Any:
							_UpperCAmelCase					=    self.get_auto_remove_tmp_dir()
							_UpperCAmelCase					=    F'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

							with patch.object(__UpperCamelCase				,   '''argv'''				,   __UpperCamelCase   ):
											run_qa.main()
											_UpperCAmelCase					=    get_results(__UpperCamelCase   )
											self.assertGreaterEqual(result['''eval_f1''']				,   3_0   )
											self.assertGreaterEqual(result['''eval_exact''']				,   3_0   )








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A   :							Optional[int]										=   logging.get_logger(__name__)

__A   :							Optional[Any]										=   {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 			_a (					lowerCAmelCase			,							lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""bit"""
			UpperCamelCase__			      =	["""preactivation""", """bottleneck"""]
			UpperCamelCase__			      =	["""SAME""", """VALID"""]



			def __init__(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[int]=3				,   __UpperCamelCase     :    Optional[int]=6_4				,   __UpperCamelCase     :    List[Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,   __UpperCamelCase     :    Any=[3, 4, 6, 3]				,   __UpperCamelCase     :    str="preactivation"				,   __UpperCamelCase     :    Union[str, Any]="relu"				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Dict=3_2				,   __UpperCamelCase     :    List[str]=0.0				,   __UpperCamelCase     :    Any=False				,   __UpperCamelCase     :    Union[str, Any]=3_2				,   __UpperCamelCase     :    List[str]=1				,   __UpperCamelCase     :    List[str]=None				,   __UpperCamelCase     :    Optional[Any]=None				,   **__UpperCamelCase     :    List[str]				,   )->List[Any]:
							super().__init__(**__UpperCamelCase   )
							if layer_type not in self.layer_types:
											raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
							if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
															_UpperCAmelCase					=    global_padding.upper()
											else:
															raise ValueError(F'Padding strategy {global_padding} not supported'   )
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    embedding_size
							_UpperCAmelCase					=    hidden_sizes
							_UpperCAmelCase					=    depths
							_UpperCAmelCase					=    layer_type
							_UpperCAmelCase					=    hidden_act
							_UpperCAmelCase					=    global_padding
							_UpperCAmelCase					=    num_groups
							_UpperCAmelCase					=    drop_path_rate
							_UpperCAmelCase					=    embedding_dynamic_padding
							_UpperCAmelCase					=    output_stride
							_UpperCAmelCase					=    width_factor

							_UpperCAmelCase					=    ['''stem'''] + [F'stage{idx}' for idx in range(1				,   len(__UpperCamelCase   ) + 1   )]
							_UpperCAmelCase		,						_UpperCAmelCase					=    get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase				,   out_indices=__UpperCamelCase				,   stage_names=self.stage_names   )



