






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__      :			int							=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		{
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  '''transfo-xl'''
				a__					       =  ['''mems''']
				a__					       =  {
				    '''n_token''': '''vocab_size''',
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}







				def __init__(						self       ,					__lowerCAmelCase=2_6_7_7_3_5       ,					__lowerCAmelCase=[2_0_0_0_0, 4_0_0_0_0, 2_0_0_0_0_0]       ,					__lowerCAmelCase=1_0_2_4       ,					__lowerCAmelCase=1_0_2_4       ,					__lowerCAmelCase=1_6       ,					__lowerCAmelCase=6_4       ,					__lowerCAmelCase=4_0_9_6       ,					__lowerCAmelCase=4       ,					__lowerCAmelCase=False       ,					__lowerCAmelCase=1_8       ,					__lowerCAmelCase=1_6_0_0       ,					__lowerCAmelCase=1_0_0_0       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=0       ,					__lowerCAmelCase=-1       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.0       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase="normal"       ,					__lowerCAmelCase=0.01       ,					__lowerCAmelCase=0.01       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=1E-5       ,					__lowerCAmelCase=0       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:int        =			vocab_size
								__magic_name__		:Optional[int]        =			[]
								self.cutoffs.extend(__lowerCAmelCase     )
								if proj_share_all_but_first:
												__magic_name__		:Any        =			[False] + [True] * len(self.cutoffs     )
								else:
												__magic_name__		:Union[str, Any]        =			[False] + [False] * len(self.cutoffs     )
								__magic_name__		:Any        =			d_model
								__magic_name__		:int        =			d_embed
								__magic_name__		:int        =			d_head
								__magic_name__		:str        =			d_inner
								__magic_name__		:str        =			div_val
								__magic_name__		:Optional[int]        =			pre_lnorm
								__magic_name__		:List[str]        =			n_layer
								__magic_name__		:Optional[int]        =			n_head
								__magic_name__		:Optional[Any]        =			mem_len
								__magic_name__		:Union[str, Any]        =			same_length
								__magic_name__		:Tuple        =			attn_type
								__magic_name__		:Union[str, Any]        =			clamp_len
								__magic_name__		:int        =			sample_softmax
								__magic_name__		:Union[str, Any]        =			adaptive
								__magic_name__		:List[str]        =			dropout
								__magic_name__		:str        =			dropatt
								__magic_name__		:Any        =			untie_r
								__magic_name__		:Tuple        =			init
								__magic_name__		:Optional[Any]        =			init_range
								__magic_name__		:Tuple        =			proj_init_std
								__magic_name__		:Optional[int]        =			init_std
								__magic_name__		:List[str]        =			layer_norm_epsilon
								super().__init__(eos_token_id=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''     )
								return -1







				@max_position_embeddings.setter
				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								raise NotImplementedError(
								    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''     )














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


SCREAMING_SNAKE_CASE__      :			List[str]							=		[
    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)

SCREAMING_SNAKE_CASE__      :			int							=		logging.getLogger()

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:int        =      argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				__magic_name__		:Tuple        =      parser.parse_args()
				return args.f

def      __lowercase					( snake_case,  snake_case="eval" ):







				"""simple docstring"""






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


SCREAMING_SNAKE_CASE__      :			Tuple							=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 						lowerCamelCase_				(      lowerCamelCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Optional[Any]        =      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()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.75     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Tuple        =      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()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertLess(result['''eval_perplexity''']       ,					1_0_0     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		: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()
												__magic_name__		:int        =      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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:Optional[Any]        =      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()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertLess(result['''eval_perplexity''']       ,					4_2     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:List[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()
												__magic_name__		:Dict        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.42     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								__magic_name__		:int        =      7 if get_gpu_count() > 1 else 2

								__magic_name__		:str        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:str        =      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()
												__magic_name__		:Any        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_accuracy''']       ,					0.75     )
												self.assertGreaterEqual(result['''eval_f1''']       ,					0.3     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.get_auto_remove_tmp_dir()
								__magic_name__		:int        =      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()
												__magic_name__		:int        =      get_results(__lowerCAmelCase     )
												self.assertGreaterEqual(result['''eval_f1''']       ,					3_0     )
												self.assertGreaterEqual(result['''eval_exact''']       ,					3_0     )






