

import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a_     =			logging.get_logger(__name__)

a_     =			{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							='conditional_detr'
			lowerCamelCase__							=['past_key_values']
			lowerCamelCase__							={
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'encoder_attention_heads',
			}
			def __init__(	self							:       List[Any]					,					a							:       List[str]=True					,					a							:       int=None					,					a							:       Optional[int]=3					,					a							:       List[str]=300					,					a							:       Optional[Any]=6					,					a							:       Tuple=2048					,					a							:       Dict=8					,					a							:       Optional[Any]=6					,					a							:       Tuple=2048					,					a							:       List[Any]=8					,					a							:       Tuple=0.0					,					a							:       Optional[Any]=0.0					,					a							:       Tuple=True					,					a							:       List[str]="relu"					,					a							:       List[Any]=256					,					a							:       str=0.1					,					a							:       Optional[Any]=0.0					,					a							:       Optional[int]=0.0					,					a							:       Union[str, Any]=0.02					,					a							:       Tuple=1.0					,					a							:       Dict=False					,					a							:       Optional[Any]="sine"					,					a							:       Optional[int]="resnet50"					,					a							:       Dict=True					,					a							:       str=False					,					a							:       int=2					,					a							:       List[str]=5					,					a							:       str=2					,					a							:       Optional[int]=1					,					a							:       Tuple=1					,					a							:       Optional[Any]=2					,					a							:       Optional[int]=5					,					a							:       List[str]=2					,					a							:       Any=0.25					,					**a							:       Tuple					,					)							->				Dict:

				"""simple docstring"""

				if backbone_config is not None and use_timm_backbone:
					raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."		)

				if not use_timm_backbone:
					if backbone_config is None:
						logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
						SCREAMING_SNAKE_CASE      :					List[str]    =				CONFIG_MAPPING["resnet"](out_features=["stage4"]		)
					elif isinstance(a					,					a		):
						SCREAMING_SNAKE_CASE      :					List[Any]    =				backbone_config.get("model_type"		)
						SCREAMING_SNAKE_CASE      :					List[str]    =				CONFIG_MAPPING[backbone_model_type]
						SCREAMING_SNAKE_CASE      :					List[Any]    =				config_class.from_dict(a		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				use_timm_backbone
				SCREAMING_SNAKE_CASE      :					Dict    =				backbone_config
				SCREAMING_SNAKE_CASE      :					Tuple    =				num_channels
				SCREAMING_SNAKE_CASE      :					str    =				num_queries
				SCREAMING_SNAKE_CASE      :					List[Any]    =				d_model
				SCREAMING_SNAKE_CASE      :					Tuple    =				encoder_ffn_dim
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				encoder_layers
				SCREAMING_SNAKE_CASE      :					Tuple    =				encoder_attention_heads
				SCREAMING_SNAKE_CASE      :					int    =				decoder_ffn_dim
				SCREAMING_SNAKE_CASE      :					Dict    =				decoder_layers
				SCREAMING_SNAKE_CASE      :					Dict    =				decoder_attention_heads
				SCREAMING_SNAKE_CASE      :					str    =				dropout
				SCREAMING_SNAKE_CASE      :					Any    =				attention_dropout
				SCREAMING_SNAKE_CASE      :					List[Any]    =				activation_dropout
				SCREAMING_SNAKE_CASE      :					List[Any]    =				activation_function
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				init_std
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				init_xavier_std
				SCREAMING_SNAKE_CASE      :					Dict    =				encoder_layerdrop
				SCREAMING_SNAKE_CASE      :					str    =				decoder_layerdrop
				SCREAMING_SNAKE_CASE      :					Any    =				encoder_layers
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				auxiliary_loss
				SCREAMING_SNAKE_CASE      :					Dict    =				position_embedding_type
				SCREAMING_SNAKE_CASE      :					Dict    =				backbone
				SCREAMING_SNAKE_CASE      :					Any    =				use_pretrained_backbone
				SCREAMING_SNAKE_CASE      :					str    =				dilation
				# Hungarian matcher
				SCREAMING_SNAKE_CASE      :					int    =				class_cost
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				bbox_cost
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				giou_cost
				# Loss coefficients
				SCREAMING_SNAKE_CASE      :					List[Any]    =				mask_loss_coefficient
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				dice_loss_coefficient
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				cls_loss_coefficient
				SCREAMING_SNAKE_CASE      :					Dict    =				bbox_loss_coefficient
				SCREAMING_SNAKE_CASE      :					Tuple    =				giou_loss_coefficient
				SCREAMING_SNAKE_CASE      :					List[Any]    =				focal_alpha
				super().__init__(is_encoder_decoder=a					,					**a		)
			@property
			def 							__UpperCamelCase       (	self							:       Dict		)							->				int:

				"""simple docstring"""

				return self.encoder_attention_heads
			@property
			def 							__UpperCamelCase       (	self							:       str		)							->				int:

				"""simple docstring"""

				return self.d_model






			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				copy.deepcopy(self.__dict__		)
				if self.backbone_config is not None:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.backbone_config.to_dict()
				SCREAMING_SNAKE_CASE      :					str    =				self.__class__.model_type
				return output






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							=version.parse('1.11'      )
			@property
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				        ("pixel_mask", {0: "batch"}),
				    ]		)
			@property
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				float:

				"""simple docstring"""

				return 1e-5






			@property
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				int:

				"""simple docstring"""

				return 12





'''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


A     =[
    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)

A     =logging.getLogger()





def 						snake_case_							():
    UpperCAmelCase			       =		argparse.ArgumentParser()
    parser.add_argument('''-f'''     )
    UpperCAmelCase			       =		parser.parse_args()
    return args.f





def 						snake_case_							(_a	:							List[str]    ,   _a	:							Union[str, Any]="eval"     ):
    UpperCAmelCase			       =		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}"     )


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







class 			_a  (	__a ):
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		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(lowercase   ,				'''argv'''   ,				lowercase				):
               run_flax_glue.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.75				)
       @slow
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		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(lowercase   ,				'''argv'''   ,				lowercase				):
               run_clm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertLess(result['''eval_perplexity''']   ,				100				)
       @slow
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		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(lowercase   ,				'''argv'''   ,				lowercase				):
               run_summarization_flax.main()
               UpperCAmelCase			       =		get_results(lowercase   ,				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     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		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(lowercase   ,				'''argv'''   ,				lowercase				):
               run_mlm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertLess(result['''eval_perplexity''']   ,				42				)
       @slow
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		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(lowercase   ,				'''argv'''   ,				lowercase				):
               run_ta_mlm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.42				)
       @slow
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		7 if get_gpu_count() > 1 else 2

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







       @slow
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		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(lowercase   ,				'''argv'''   ,				lowercase				):
               run_qa.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_f1''']   ,				30				)
               self.assertGreaterEqual(result['''eval_exact''']   ,				30				)




