


import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
   import os

   from flax.core.frozen_dict import unfreeze
   from flax.traverse_util import flatten_dict

   from transformers import FlaxBertModel

   UpperCAmelCase_				:			str   =	'0.12'  # assumed parallelism: 8
@require_flax
@is_staging_test
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	Optional[Any]					)		->      Dict:
             a_ :		Dict							=  TOKEN
             HfFolder.save_token(SCREAMING_SNAKE_CASE__					)
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	Tuple					)		->      str:
             try:
                    delete_repo(token=cls._token					,							repo_id='test-model-flax'					)
             except HTTPError:
                    pass

             try:
                    delete_repo(token=cls._token					,							repo_id='valid_org/test-model-flax-org'					)
             except HTTPError:
                    pass
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[Any]:
             a_ :		Any							=  BertConfig(
                 vocab_size=9_9					,							hidden_size=3_2					,							num_hidden_layers=5					,							num_attention_heads=4					,							intermediate_size=3_7					)
             a_ :		Tuple							=  FlaxBertModel(SCREAMING_SNAKE_CASE__					)
             model.push_to_hub('test-model-flax'					,							use_auth_token=self._token					)

             a_ :		Optional[int]							=  FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""					)

             a_ :		List[str]							=  flatten_dict(unfreeze(model.params					)					)
             a_ :		Optional[Any]							=  flatten_dict(unfreeze(new_model.params					)					)

             for key in base_params.keys():
                    a_ :		Optional[Any]							=  (base_params[key] - new_params[key]).sum().item()
                    self.assertLessEqual(SCREAMING_SNAKE_CASE__					,							1E-3					,							msg=F"""{key} not identical"""					)

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

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

             a_ :		Optional[int]							=  FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""					)

             a_ :		Union[str, Any]							=  flatten_dict(unfreeze(model.params					)					)
             a_ :		Optional[Any]							=  flatten_dict(unfreeze(new_model.params					)					)

             for key in base_params.keys():
                    a_ :		Any							=  (base_params[key] - new_params[key]).sum().item()
                    self.assertLessEqual(SCREAMING_SNAKE_CASE__					,							1E-3					,							msg=F"""{key} not identical"""					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      str:
             a_ :		Optional[Any]							=  BertConfig(
                 vocab_size=9_9					,							hidden_size=3_2					,							num_hidden_layers=5					,							num_attention_heads=4					,							intermediate_size=3_7					)
             a_ :		Union[str, Any]							=  FlaxBertModel(SCREAMING_SNAKE_CASE__					)
             model.push_to_hub('valid_org/test-model-flax-org'					,							use_auth_token=self._token					)

             a_ :		Tuple							=  FlaxBertModel.from_pretrained('valid_org/test-model-flax-org'					)

             a_ :		Optional[Any]							=  flatten_dict(unfreeze(model.params					)					)
             a_ :		Tuple							=  flatten_dict(unfreeze(new_model.params					)					)

             for key in base_params.keys():
                    a_ :		Any							=  (base_params[key] - new_params[key]).sum().item()
                    self.assertLessEqual(SCREAMING_SNAKE_CASE__					,							1E-3					,							msg=F"""{key} not identical"""					)

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

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

             a_ :		Optional[Any]							=  FlaxBertModel.from_pretrained('valid_org/test-model-flax-org'					)

             a_ :		Optional[int]							=  flatten_dict(unfreeze(model.params					)					)
             a_ :		Union[str, Any]							=  flatten_dict(unfreeze(new_model.params					)					)

             for key in base_params.keys():
                    a_ :		Optional[int]							=  (base_params[key] - new_params[key]).sum().item()
                    self.assertLessEqual(SCREAMING_SNAKE_CASE__					,							1E-3					,							msg=F"""{key} not identical"""					)







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[str]  ,   __A			:      List[Any]    )     ->		Dict:

       """simple docstring"""

       a_ :		int							=  True
       a_ :		Dict							=  flatten_dict(modela.params    )
       a_ :		Union[str, Any]							=  flatten_dict(modela.params    )
       for key in flat_params_a.keys():
              if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]    )    ) > 1e-4:
                     a_ :		Union[str, Any]							=  False

       return models_are_equal
@require_flax
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Optional[Any]:
             a_ :		int							=  BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'					)
             a_ :		List[Any]							=  FlaxBertModel(SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[int]							=  'bert'
             with tempfile.TemporaryDirectory() as tmp_dir:
                    model.save_pretrained(os.path.join(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)					)

                    with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                           a_ :		List[Any]							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					)

                    a_ :		Dict							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					,							subfolder=SCREAMING_SNAKE_CASE__					)

             self.assertTrue(check_models_equal(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[Any]:
             a_ :		int							=  BertConfig.from_pretrained('hf-internal-testing/tiny-bert-flax-only'					)
             a_ :		int							=  FlaxBertModel(SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[int]							=  'bert'
             with tempfile.TemporaryDirectory() as tmp_dir:
                    model.save_pretrained(os.path.join(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)					,							max_shard_size='10KB'					)

                    with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                           a_ :		Optional[Any]							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					)

                    a_ :		Union[str, Any]							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					,							subfolder=SCREAMING_SNAKE_CASE__					)

             self.assertTrue(check_models_equal(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[Any]:
             a_ :		Any							=  'bert'
             a_ :		str							=  'hf-internal-testing/tiny-random-bert-subfolder'

             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    a_ :		Any							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					)

             a_ :		str							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					,							subfolder=SCREAMING_SNAKE_CASE__					)

             self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Optional[Any]:
             a_ :		List[Any]							=  'bert'
             a_ :		Any							=  'hf-internal-testing/tiny-random-bert-sharded-subfolder'
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    a_ :		Dict							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					)

             a_ :		List[str]							=  FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE__					,							subfolder=SCREAMING_SNAKE_CASE__					)

             self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)






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					)


