






"""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


lowerCamelCase_							      = [
    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)

lowerCamelCase_							      = logging.getLogger()

def 							snake_case (  ):
			UpperCAmelCase_	:					int     	=   argparse.ArgumentParser()
			parser.add_argument("-f"		)
			UpperCAmelCase_	:					Optional[Any]     	=   parser.parse_args()
			return args.f

def 							snake_case (  A__      ,A__="eval"		):
			UpperCAmelCase_	:					Union[str, 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}"""		)


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







class      UpperCamelCase_			(__A	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     List[str]:
				UpperCAmelCase_	:					Dict     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					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()
							UpperCAmelCase_	:					List[str]     	=   get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_accuracy"]      ,      0.7_5		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Dict:
				UpperCAmelCase_	:					Dict     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					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()
							UpperCAmelCase_	:					Optional[Any]     	=   get_results(lowerCAmelCase_		)
							self.assertLess(result["eval_perplexity"]      ,      100		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					List[Any]     	=   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()
							UpperCAmelCase_	:					Dict     	=   get_results(lowerCAmelCase_      ,      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 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					List[str]     	=   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()
							UpperCAmelCase_	:					Dict     	=   get_results(lowerCAmelCase_		)
							self.assertLess(result["eval_perplexity"]      ,      42		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					Dict     	=   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()
							UpperCAmelCase_	:					str     	=   get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_accuracy"]      ,      0.4_2		)





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

				UpperCAmelCase_	:					Optional[int]     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					Dict     	=   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()
							UpperCAmelCase_	:					Tuple     	=   get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_accuracy"]      ,      0.7_5		)
							self.assertGreaterEqual(result["eval_f1"]      ,      0.3		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Optional[Any]:
				UpperCAmelCase_	:					Dict     	=   self.get_auto_remove_tmp_dir()
				UpperCAmelCase_	:					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()
							UpperCAmelCase_	:					List[str]     	=   get_results(lowerCAmelCase_		)
							self.assertGreaterEqual(result["eval_f1"]      ,      30		)
							self.assertGreaterEqual(result["eval_exact"]      ,      30		)





"""simple docstring"""



import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402


UpperCamelCase__          =	{
    'return_dict': False,
    'output_hidden_states': True,
    'output_attentions': True,
    'torchscript': True,
    'torch_dtype': 'float16',
    'use_bfloat16': True,
    'tf_legacy_loss': True,
    'pruned_heads': {'a': 1},
    'tie_word_embeddings': False,
    'is_decoder': True,
    'cross_attention_hidden_size': 1_28,
    'add_cross_attention': True,
    'tie_encoder_decoder': True,
    'max_length': 50,
    'min_length': 3,
    'do_sample': True,
    'early_stopping': True,
    'num_beams': 3,
    'num_beam_groups': 3,
    'diversity_penalty': 0.5,
    'temperature': 2.0,
    'top_k': 10,
    'top_p': 0.7,
    'typical_p': 0.2,
    'repetition_penalty': 0.8,
    'length_penalty': 0.8,
    'no_repeat_ngram_size': 5,
    'encoder_no_repeat_ngram_size': 5,
    'bad_words_ids': [1, 2, 3],
    'num_return_sequences': 3,
    'chunk_size_feed_forward': 5,
    'output_scores': True,
    'return_dict_in_generate': True,
    'forced_bos_token_id': 2,
    'forced_eos_token_id': 3,
    'remove_invalid_values': True,
    'architectures': ['BertModel'],
    'finetuning_task': 'translation',
    'id2label': {0: 'label'},
    'label2id': {'label': '0'},
    'tokenizer_class': 'BertTokenizerFast',
    'prefix': 'prefix',
    'bos_token_id': 6,
    'pad_token_id': 7,
    'eos_token_id': 8,
    'sep_token_id': 9,
    'decoder_start_token_id': 10,
    'exponential_decay_length_penalty': (5, 1.01),
    'suppress_tokens': [0, 1],
    'begin_suppress_tokens': 2,
    'task_specific_params': {'translation': 'some_params'},
    'problem_type': 'regression',
}





@is_staging_test
class 				a			(					unittest.TestCase      ):



      @classmethod
      def  __snake_case							(   cls      ):
          UpperCAmelCase__	:      List[str]     							=					TOKEN
          HfFolder.save_token(UpperCamelCase_      )



      @classmethod
      def  __snake_case							(   cls      ):
          try:
              delete_repo(token=cls._token     ,	repo_id='test-config'      )
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token     ,	repo_id='valid_org/test-config-org'      )
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token     ,	repo_id='test-dynamic-config'      )
          except HTTPError:
              pass



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					BertConfig(
              vocab_size=99     ,	hidden_size=32     ,	num_hidden_layers=5     ,	num_attention_heads=4     ,	intermediate_size=37      )
          config.push_to_hub('test-config'     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Optional[int]     							=					BertConfig.from_pretrained(F'''{USER}/test-config'''      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )

        # Reset repo
          delete_repo(token=self._token     ,	repo_id='test-config'      )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(UpperCamelCase_     ,	repo_id='test-config'     ,	push_to_hub=UpperCamelCase_     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained(F'''{USER}/test-config'''      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[Any]     							=					BertConfig(
              vocab_size=99     ,	hidden_size=32     ,	num_hidden_layers=5     ,	num_attention_heads=4     ,	intermediate_size=37      )
          config.push_to_hub('valid_org/test-config-org'     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained('valid_org/test-config-org'      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )

        # Reset repo
          delete_repo(token=self._token     ,	repo_id='valid_org/test-config-org'      )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(
                  UpperCamelCase_     ,	repo_id='valid_org/test-config-org'     ,	push_to_hub=UpperCamelCase_     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Optional[Any]     							=					BertConfig.from_pretrained('valid_org/test-config-org'      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )



      def  __snake_case							(   self      ):
          CustomConfig.register_for_auto_class()
          UpperCAmelCase__	:      Dict     							=					CustomConfig(attribute=42      )

          config.push_to_hub('test-dynamic-config'     ,	use_auth_token=self._token      )

          # This has added the proper auto_map field to the config
          self.assertDictEqual(config.auto_map     ,	{'AutoConfig': 'custom_configuration.CustomConfig'}      )

          UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config'''     ,	trust_remote_code=UpperCamelCase_      )
          # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
          self.assertEqual(new_config.__class__.__name__     ,	'CustomConfig'      )
          self.assertEqual(new_config.attribute     ,	42      )





class 				a			(					unittest.TestCase      ):



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[str]     							=					GPTaConfig()

          # attempt to modify each of int/float/bool/str config records and verify they were updated
          UpperCAmelCase__	:      List[str]     							=					c.n_embd + 1  # int
          UpperCAmelCase__	:      Optional[int]     							=					c.resid_pdrop + 1.0  # float
          UpperCAmelCase__	:      Optional[int]     							=					not c.scale_attn_weights  # bool
          UpperCAmelCase__	:      str     							=					c.summary_type + 'foo'  # str
          c.update_from_string(
              F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'''      )
          self.assertEqual(UpperCamelCase_     ,	c.n_embd     ,	'mismatch for key: n_embd'      )
          self.assertEqual(UpperCamelCase_     ,	c.resid_pdrop     ,	'mismatch for key: resid_pdrop'      )
          self.assertEqual(UpperCamelCase_     ,	c.scale_attn_weights     ,	'mismatch for key: scale_attn_weights'      )
          self.assertEqual(UpperCamelCase_     ,	c.summary_type     ,	'mismatch for key: summary_type'      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					PretrainedConfig()
          UpperCAmelCase__	:      str     							=					[key for key in base_config.__dict__ if key not in config_common_kwargs]
          # If this part of the test fails, you have arguments to addin config_common_kwargs above.
          self.assertListEqual(
              UpperCamelCase_     ,	['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version']      )
          UpperCAmelCase__	:      int     							=					[key for key, value in config_common_kwargs.items() if value == getattr(UpperCamelCase_     ,	UpperCamelCase_      )]
          if len(UpperCamelCase_      ) > 0:
              raise ValueError(
                  'The following keys are set with the default values in'
                  ' `test_configuration_common.config_common_kwargs` pick another value for them:'
                  F''' {', '.join(UpperCamelCase_      )}.'''      )



      def  __snake_case							(   self      ):
          with self.assertRaises(UpperCamelCase_      ):
              # config is in subfolder, the following should not work without specifying the subfolder
              UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'      )

          UpperCAmelCase__	:      str     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'     ,	subfolder='bert'      )

          self.assertIsNotNone(UpperCamelCase_      )



      def  __snake_case							(   self      ):
          # A mock response for an HTTP head request to emulate server down
          UpperCAmelCase__	:      str     							=					mock.Mock()
          UpperCAmelCase__	:      List[Any]     							=					500
          UpperCAmelCase__	:      Optional[int]     							=					{}
          UpperCAmelCase__	:      Dict     							=					HTTPError
          UpperCAmelCase__	:      Optional[int]     							=					{}

          # Download this model to make sure it's in the cache.
          UpperCAmelCase__	:      List[str]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'      )

          # Under the mock environment we get a 500 error when trying to reach the model.
          with mock.patch('requests.Session.request'     ,	return_value=UpperCamelCase_      ) as mock_head:
              UpperCAmelCase__	:      Optional[Any]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'      )
              # This check we did call the fake head request
              mock_head.assert_called()



      def  __snake_case							(   self      ):
          # This test is for deprecated behavior and can be removed in v5
          UpperCAmelCase__	:      List[str]     							=					BertConfig.from_pretrained(
              'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					AutoConfig.from_pretrained('bert-base-cased'      )
          UpperCAmelCase__	:      str     							=					['config.4.0.0.json']

          with tempfile.TemporaryDirectory() as tmp_dir:
              configuration.save_pretrained(UpperCamelCase_      )
              UpperCAmelCase__	:      List[str]     							=					2
              json.dump(configuration.to_dict()     ,	open(os.path.join(UpperCamelCase_     ,	'config.4.0.0.json'      )     ,	'w'      )      )

              # This should pick the new configuration file as the version of Transformers is > 4.0.0
              UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(UpperCamelCase_      )
              self.assertEqual(new_configuration.hidden_size     ,	2      )

              # Will need to be adjusted if we reach v42 and this test is still here.
              # Should pick the old configuration file as the version of Transformers is < 4.42.0
              UpperCAmelCase__	:      Tuple     							=					['config.42.0.0.json']
              UpperCAmelCase__	:      Tuple     							=					768
              configuration.save_pretrained(UpperCamelCase_      )
              shutil.move(os.path.join(UpperCamelCase_     ,	'config.4.0.0.json'      )     ,	os.path.join(UpperCamelCase_     ,	'config.42.0.0.json'      )      )
              UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(UpperCamelCase_      )
              self.assertEqual(new_configuration.hidden_size     ,	768      )



      def  __snake_case							(   self      ):
          # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
          UpperCAmelCase__	:      Tuple     							=					'hf-internal-testing/test-two-configs'

          import transformers as new_transformers

          UpperCAmelCase__	:      Optional[int]     							=					'v4.0.0'
          UpperCAmelCase__							,							UpperCAmelCase__	:      Any     							=					new_transformers.models.auto.AutoConfig.from_pretrained(
              UpperCamelCase_     ,	return_unused_kwargs=UpperCamelCase_      )
          self.assertEqual(new_configuration.hidden_size     ,	2      )
          # This checks `_configuration_file` ia not kept in the kwargs by mistake.
          self.assertDictEqual(UpperCamelCase_     ,	{}      )

          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers

          UpperCAmelCase__	:      List[Any]     							=					'v3.0.0'
          UpperCAmelCase__	:      Union[str, Any]     							=					old_transformers.models.auto.AutoConfig.from_pretrained(UpperCamelCase_      )
          self.assertEqual(old_configuration.hidden_size     ,	768      )

