


import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      str  ,   __A			:      str  ,   __A			:      Path  ,   __A			:      str = None  ,   __A			:      str = None  ,   __A			:      str = None  ,   )     ->		Any:

       """simple docstring"""

       if config_name_or_path is None:
              a_ :		Tuple							=  'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

       if generator_tokenizer_name_or_path is None:
              a_ :		str							=  generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              a_ :		List[Any]							=  question_encoder_name_or_path

       a_ :		Any							=  RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

       # Save model.
       a_ :		Any							=  RagConfig.from_pretrained(__A    )
       a_ :		Any							=  AutoConfig.from_pretrained(__A    )
       a_ :		Tuple							=  AutoConfig.from_pretrained(__A    )

       a_ :		int							=  gen_config
       a_ :		Any							=  question_encoder_config

       a_ :		Dict							=  model_class.from_pretrained_question_encoder_generator(
           __A  ,   __A  ,   config=__A    )
       rag_model.save_pretrained(__A    )

       # Sanity check.
       model_class.from_pretrained(__A    )

       # Save tokenizers.
       a_ :		List[Any]							=  AutoTokenizer.from_pretrained(__A    )
       gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'    )
       a_ :		Union[str, Any]							=  AutoTokenizer.from_pretrained(__A    )
       question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'    )


if __name__ == "__main__":
   UpperCAmelCase_				:			Dict   =	argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   UpperCAmelCase_				:			int   =	parser.parse_args()

   UpperCAmelCase_				:			str   =	Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )






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_				:			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)

UpperCAmelCase_				:			Optional[int]   =	logging.getLogger()







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  argparse.ArgumentParser()
       parser.add_argument('-f'    )
       a_ :		Optional[Any]							=  parser.parse_args()
       return args.f







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      List[Any]="eval"    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		List[Any]							=  os.path.join(__A  ,   F"""{split}_results.json"""    )
       if os.path.exists(__A    ):
              with open(__A  ,   'r'    ) as f:
                     return json.load(__A    )
       raise ValueError(F"""can't find {path}"""    )


UpperCAmelCase_				:			str   =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Tuple:
             a_ :		Optional[Any]							=  self.get_auto_remove_tmp_dir()
             a_ :		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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_glue.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      str:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		Union[str, 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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_clm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							1_0_0					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
             a_ :		Tuple							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_summarization_flax.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					,							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 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		int							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_mlm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							4_2					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[str]:
             a_ :		str							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_ta_mlm_flax.main()
                    a_ :		Dict							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.42					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
             a_ :		int							=  7 if get_gpu_count() > 1 else 2

             a_ :		Dict							=  self.get_auto_remove_tmp_dir()
             a_ :		Tuple							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_ner.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
                    self.assertGreaterEqual(result['eval_f1']					,							0.3					)

      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      int:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_qa.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_f1']					,							3_0					)
                    self.assertGreaterEqual(result['eval_exact']					,							3_0					)


