

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


__snake_case    :    Tuple         =     [
    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)

__snake_case    :    Dict         =     logging.getLogger()







def 	_UpperCamelCase   (    )  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						argparse.ArgumentParser()
						parser.add_argument('-f'		)
						lowerCAmelCase__    						=						parser.parse_args()
						return args.f







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Any="eval"		)  ->   str:

						"""simple docstring"""



						lowerCAmelCase__    						=						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}"		)


__snake_case    :    Optional[int]         =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class     __SCREAMING_SNAKE_CASE					(   __lowercase):






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.get_auto_remove_tmp_dir()
												lowerCAmelCase__    						=						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(_UpperCAmelCase     ,						'argv'     ,						_UpperCAmelCase   ):
																		run_flax_glue.main()
																		lowerCAmelCase__    						=						get_results(_UpperCAmelCase   )
																		self.assertGreaterEqual(result['eval_accuracy']     ,						0.75   )






						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.get_auto_remove_tmp_dir()
												lowerCAmelCase__    						=						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(_UpperCAmelCase     ,						'argv'     ,						_UpperCAmelCase   ):
																		run_clm_flax.main()
																		lowerCAmelCase__    						=						get_results(_UpperCAmelCase   )
																		self.assertLess(result['eval_perplexity']     ,						1_00   )






						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.get_auto_remove_tmp_dir()
												lowerCAmelCase__    						=						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(_UpperCAmelCase     ,						'argv'     ,						_UpperCAmelCase   ):
																		run_summarization_flax.main()
																		lowerCAmelCase__    						=						get_results(_UpperCAmelCase     ,						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"""
												lowerCAmelCase__    						=						self.get_auto_remove_tmp_dir()
												lowerCAmelCase__    						=						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(_UpperCAmelCase     ,						'argv'     ,						_UpperCAmelCase   ):
																		run_mlm_flax.main()
																		lowerCAmelCase__    						=						get_results(_UpperCAmelCase   )
																		self.assertLess(result['eval_perplexity']     ,						42   )






						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.get_auto_remove_tmp_dir()
												lowerCAmelCase__    						=						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(_UpperCAmelCase     ,						'argv'     ,						_UpperCAmelCase   ):
																		run_ta_mlm_flax.main()
																		lowerCAmelCase__    						=						get_results(_UpperCAmelCase   )
																		self.assertGreaterEqual(result['eval_accuracy']     ,						0.42   )






						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						7 if get_gpu_count() > 1 else 2

												lowerCAmelCase__    						=						self.get_auto_remove_tmp_dir()
												lowerCAmelCase__    						=						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(_UpperCAmelCase     ,						'argv'     ,						_UpperCAmelCase   ):
																		run_flax_ner.main()
																		lowerCAmelCase__    						=						get_results(_UpperCAmelCase   )
																		self.assertGreaterEqual(result['eval_accuracy']     ,						0.75   )
																		self.assertGreaterEqual(result['eval_f1']     ,						0.3   )







						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.get_auto_remove_tmp_dir()
												lowerCAmelCase__    						=						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(_UpperCAmelCase     ,						'argv'     ,						_UpperCAmelCase   ):
																		run_qa.main()
																		lowerCAmelCase__    						=						get_results(_UpperCAmelCase   )
																		self.assertGreaterEqual(result['eval_f1']     ,						30   )
																		self.assertGreaterEqual(result['eval_exact']     ,						30   )













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case    :    Tuple         =     {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Dict         =     ["""GLPNFeatureExtractor"""]
					__snake_case    :    Optional[Any]         =     ["""GLPNImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Tuple         =     [
					    """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GLPNForDepthEstimation""",
					    """GLPNLayer""",
					    """GLPNModel""",
					    """GLPNPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__snake_case    :    Optional[int]         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





