





'''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


_lowercase       						=			[
    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)

_lowercase       						=			logging.getLogger()

def    __UpperCamelCase	(	) ->Tuple:
 snake_case				=			argparse.ArgumentParser()
 parser.add_argument('''-f'''     )
 snake_case				=			parser.parse_args()
 return args.f

def    __UpperCamelCase	(	a    : Dict					,   a    : Tuple="eval"     ) ->List[Any]:
 snake_case				=			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}"""     )


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






class 			_lowercase						(       __a					):







     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			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(A__    ,   '''argv'''    ,   A__    ):
       run_flax_glue.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.7_5    )







     @slow
     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			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(A__    ,   '''argv'''    ,   A__    ):
       run_clm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertLess(result['''eval_perplexity''']    ,   1_00    )







     @slow
     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			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(A__    ,   '''argv'''    ,   A__    ):
       run_summarization_flax.main()
       snake_case				=			get_results(A__    ,   split='''test'''    )
       self.assertGreaterEqual(result['''test_rouge1''']    ,   10    )
       self.assertGreaterEqual(result['''test_rouge2''']    ,   2    )
       self.assertGreaterEqual(result['''test_rougeL''']    ,   7    )
       self.assertGreaterEqual(result['''test_rougeLsum''']    ,   7    )







     @slow
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			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(A__    ,   '''argv'''    ,   A__    ):
       run_mlm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertLess(result['''eval_perplexity''']    ,   42    )







     @slow
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			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(A__    ,   '''argv'''    ,   A__    ):
       run_ta_mlm_flax.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.4_2    )







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

      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			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(A__    ,   '''argv'''    ,   A__    ):
       run_flax_ner.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.7_5    )
       self.assertGreaterEqual(result['''eval_f1''']    ,   0.3    )







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			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(A__    ,   '''argv'''    ,   A__    ):
       run_qa.main()
       snake_case				=			get_results(A__    )
       self.assertGreaterEqual(result['''eval_f1''']    ,   30    )
       self.assertGreaterEqual(result['''eval_exact''']    ,   30    )








'''simple docstring'''







import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
   import torch
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					'''char'''
     _UpperCAmelCase										=					'''bpe'''
     _UpperCAmelCase										=					'''wp'''




_lowercase       						=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					['''image_processor''', '''char_tokenizer''']
     _UpperCAmelCase										=					'''ViTImageProcessor'''
     _UpperCAmelCase										=					'''MgpstrTokenizer'''







     def __init__( self    ,   A__=None    ,   A__=None    ,   **A__    ) ->	List[Any]:
      snake_case				=			None
      if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''    ,   A__    ,   )
       snake_case				=			kwargs.pop('''feature_extractor'''    )

      snake_case				=			image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.'''    )
      if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.'''    )

      snake_case				=			tokenizer
      snake_case				=			AutoTokenizer.from_pretrained('''gpt2'''    )
      snake_case				=			AutoTokenizer.from_pretrained('''bert-base-uncased'''    )

      super().__init__(A__    ,   A__    )







     def __call__( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   **A__    ) ->	List[str]:
      if images is None and text is None:
       raise ValueError('''You need to specify either an `images` or `text` input to process.'''    )

      if images is not None:
       snake_case				=			self.image_processor(A__    ,   return_tensors=A__    ,   **A__    )
      if text is not None:
       snake_case				=			self.char_tokenizer(A__    ,   return_tensors=A__    ,   **A__    )

      if text is None:
       return inputs
      elif images is None:
       return encodings
      else:
       snake_case				=			encodings['''input_ids''']
       return inputs







     def     UpperCamelCase			( self    ,   A__    ) ->	Dict:
      snake_case				,		snake_case				,		snake_case				=			sequences
      snake_case				=			char_preds.size(0    )

      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''char'''    )
      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''bpe'''    )
      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''wp'''    )

      snake_case				=			[]
      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
       snake_case				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
       snake_case				=			scores.index(max(A__    )    )
       final_strs.append(strs[max_score_index]    )
       final_scores.append(scores[max_score_index]    )

      snake_case				=			{}
      snake_case				=			final_strs
      snake_case				=			final_scores
      snake_case				=			char_strs
      snake_case				=			bpe_strs
      snake_case				=			wp_strs
      return out







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	Optional[Any]:
      if format == DecodeType.CHARACTER:
       snake_case				=			self.char_decode
       snake_case				=			1
       snake_case				=			'''[s]'''
      elif format == DecodeType.BPE:
       snake_case				=			self.bpe_decode
       snake_case				=			2
       snake_case				=			'''#'''
      elif format == DecodeType.WORDPIECE:
       snake_case				=			self.wp_decode
       snake_case				=			1_02
       snake_case				=			'''[SEP]'''
      else:
       raise ValueError(F"""Format {format} is not supported."""    )

      snake_case				,		snake_case				=			[], []
      snake_case				=			pred_logits.size(0    )
      snake_case				=			pred_logits.size(1    )
      snake_case				,		snake_case				=			pred_logits.topk(1    ,   dim=-1    ,   largest=A__    ,   sorted=A__    )
      snake_case				=			preds_index.view(-1    ,   A__    )[:, 1:]
      snake_case				=			decoder(A__    )
      snake_case				,		snake_case				=			torch.nn.functional.softmax(A__    ,   dim=2    ).max(dim=2    )
      snake_case				=			preds_max_prob[:, 1:]

      for index in range(A__    ):
       snake_case				=			preds_str[index].find(A__    )
       snake_case				=			preds_str[index][:pred_eos]
       snake_case				=			preds_index[index].cpu().tolist()
       snake_case				=			pred_index.index(A__    ) if eos_token in pred_index else -1
       snake_case				=			preds_max_prob[index][: pred_eos_index + 1]
       snake_case				=			pred_max_prob.cumprod(dim=0    )[-1] if pred_max_prob.nelement() != 0 else 0.0
       dec_strs.append(A__    )
       conf_scores.append(A__    )

      return dec_strs, conf_scores







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      snake_case				=			[seq.replace(''' '''    ,   ''''''    ) for seq in self.char_tokenizer.batch_decode(A__    )]
      return decode_strs







     def     UpperCamelCase			( self    ,   A__    ) ->	List[str]:
      return self.bpe_tokenizer.batch_decode(A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			[seq.replace(''' '''    ,   ''''''    ) for seq in self.wp_tokenizer.batch_decode(A__    )]
      return decode_strs

