






"""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       __UpperCamelCase						(		):
	A_		:    List[str]						     = argparse.ArgumentParser()
	parser.add_argument("""-f"""       )
	A_		:    Optional[int]						     = parser.parse_args()
	return args.f






def       __UpperCamelCase						(		snake_case__ , snake_case__="eval"       ):
	A_		:    Dict						     = os.path.join(snake_case__ , F"""{split}_results.json"""       )
	if os.path.exists(snake_case__       ):
		with open(snake_case__ , """r"""       ) as f:
			return json.load(snake_case__       )
	raise ValueError(F"""can't find {path}"""       )


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




class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		A_		:    int						     = self.get_auto_remove_tmp_dir()
		A_		:    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(lowerCAmelCase_  ,  """argv"""  ,  lowerCAmelCase_				):
			run_flax_glue.main()
			A_		:    Tuple						     = get_results(lowerCAmelCase_				)
			self.assertGreaterEqual(result["""eval_accuracy"""]  ,  0.75				)





	@slow
	def  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = self.get_auto_remove_tmp_dir()
		A_		:    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()
			A_		:    List[Any]						     = get_results(lowerCAmelCase_				)
			self.assertLess(result["""eval_perplexity"""]  ,  100				)





	@slow
	def  lowerCamelCase(self				):
		A_		:    Any						     = self.get_auto_remove_tmp_dir()
		A_		:    Optional[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()
			A_		:    List[Any]						     = 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  lowerCamelCase(self				):
		A_		:    List[str]						     = self.get_auto_remove_tmp_dir()
		A_		:    Any						     = 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()
			A_		:    Optional[int]						     = get_results(lowerCAmelCase_				)
			self.assertLess(result["""eval_perplexity"""]  ,  42				)





	@slow
	def  lowerCamelCase(self				):
		A_		:    List[str]						     = self.get_auto_remove_tmp_dir()
		A_		:    int						     = 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()
			A_		:    str						     = get_results(lowerCAmelCase_				)
			self.assertGreaterEqual(result["""eval_accuracy"""]  ,  0.42				)





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

		A_		:    Tuple						     = 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(lowerCAmelCase_  ,  """argv"""  ,  lowerCAmelCase_				):
			run_flax_ner.main()
			A_		:    List[str]						     = get_results(lowerCAmelCase_				)
			self.assertGreaterEqual(result["""eval_accuracy"""]  ,  0.75				)
			self.assertGreaterEqual(result["""eval_f1"""]  ,  0.3				)





	@slow
	def  lowerCamelCase(self				):
		A_		:    List[str]						     = self.get_auto_remove_tmp_dir()
		A_		:    Any						     = 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()
			A_		:    Dict						     = get_results(lowerCAmelCase_				)
			self.assertGreaterEqual(result["""eval_f1"""]  ,  30				)
			self.assertGreaterEqual(result["""eval_exact"""]  ,  30				)









"""simple docstring"""
from typing import Dict

from .base import GenericTensor, Pipeline



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if tokenize_kwargs is None:
			A_		:    Dict						     = {}

		if truncation is not None:
			if "truncation" in tokenize_kwargs:
				raise ValueError(
				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""				)
			A_		:    Optional[Any]						     = truncation

		A_		:    Optional[int]						     = tokenize_kwargs

		A_		:    Optional[Any]						     = {}
		if return_tensors is not None:
			A_		:    Optional[Any]						     = return_tensors

		return preprocess_params, {}, postprocess_params





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				):
		A_		:    List[Any]						     = self.framework
		A_		:    Optional[int]						     = self.tokenizer(lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_  ,  **lowerCAmelCase_				)
		return model_inputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.model(**lowerCAmelCase_				)
		return model_outputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		# [0] is the first available tensor, logits or last_hidden_state.
		if return_tensors:
			return model_outputs[0]
		if self.framework == "pt":
			return model_outputs[0].tolist()
		elif self.framework == "tf":
			return model_outputs[0].numpy().tolist()





	def __call__(self  ,  *lowerCAmelCase_  ,  **lowerCAmelCase_				):
		return super().__call__(*lowerCAmelCase_  ,  **lowerCAmelCase_				)

