

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_            = [
    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_            = logging.getLogger()

def 							__SCREAMING_SNAKE_CASE							(			)				->    Dict:
				_UpperCAmelCase    :						str							=      argparse.ArgumentParser()
				parser.add_argument("-f"	)
				_UpperCAmelCase    :						int							=      parser.parse_args()
				return args.f

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					Dict="eval"	)				->    Union[str, Any]:
				_UpperCAmelCase    :						Dict							=      os.path.join(lowerCAmelCase					, F'{split}_results.json'	)
				if os.path.exists(lowerCAmelCase	):
								with open(lowerCAmelCase					, "r"	) as f:
												return json.load(lowerCAmelCase	)
				raise ValueError(F'can\'t find {path}'	)


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





class 			a					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Tuple							=      f'\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_flax_glue.main()
														_UpperCAmelCase    :						Union[str, Any]							=      get_results(A_		)
														self.assertGreaterEqual(result["eval_accuracy"]  ,   0.75		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Optional[Any]							=      f'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_clm_flax.main()
														_UpperCAmelCase    :						Any							=      get_results(A_		)
														self.assertLess(result["eval_perplexity"]  ,   100		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						List[Any]							=      f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_summarization_flax.main()
														_UpperCAmelCase    :						Optional[Any]							=      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		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Optional[Any]							=      f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_mlm_flax.main()
														_UpperCAmelCase    :						Tuple							=      get_results(A_		)
														self.assertLess(result["eval_perplexity"]  ,   42		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Union[str, Any]							=      f'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_ta_mlm_flax.main()
														_UpperCAmelCase    :						List[Any]							=      get_results(A_		)
														self.assertGreaterEqual(result["eval_accuracy"]  ,   0.42		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      7 if get_gpu_count() > 1 else 2

										_UpperCAmelCase    :						int							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						Tuple							=      f'\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_flax_ner.main()
														_UpperCAmelCase    :						Tuple							=      get_results(A_		)
														self.assertGreaterEqual(result["eval_accuracy"]  ,   0.75		)
														self.assertGreaterEqual(result["eval_f1"]  ,   0.3		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_auto_remove_tmp_dir()
										_UpperCAmelCase    :						str							=      f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

										with patch.object(A_  ,   "argv"  ,   A_		):
														run_qa.main()
														_UpperCAmelCase    :						Union[str, Any]							=      get_results(A_		)
														self.assertGreaterEqual(result["eval_f1"]  ,   30		)
														self.assertGreaterEqual(result["eval_exact"]  ,   30		)





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


