
"""simple docstring"""




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map


def     __magic_name__      ( __snake_case							:			Dict[str, torch.Tensor]    )					->							Dict:
       lowercase       :							Dict									=				[]
       lowercase       :							Optional[int]									=				[]
       lowercase       :							List[Any]									=				[]

       for rt in rc.restypes:
              lowercase       :							int									=				rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
              restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]    )
              lowercase       :							Any									=				{name: i for i, name in enumerate(UpperCamelCase__    )}
              restype_atomaa_to_atomaa_list.append(
                  [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]    )

              restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]    )

       # Add dummy mapping for restype 'UNK'
       restype_atomaa_to_atomaa_list.append([0] * 14    )
       restype_atomaa_to_atomaa_list.append([0] * 37    )
       restype_atomaa_mask_list.append([0.0] * 14    )

       lowercase       :							Optional[Any]									=				torch.tensor(
           UpperCamelCase__	,					dtype=torch.intaa	,					device=protein["aatype"].device	,					)
       lowercase       :							str									=				torch.tensor(
           UpperCamelCase__	,					dtype=torch.intaa	,					device=protein["aatype"].device	,					)
       lowercase       :							Any									=				torch.tensor(
           UpperCamelCase__	,					dtype=torch.floataa	,					device=protein["aatype"].device	,					)
       lowercase       :							str									=				protein["aatype"].to(torch.long    )

       # create the mapping for (residx, atom14) --> atom37, i.e. an array
       # with shape (num_res, 14) containing the atom37 indices for this protein
       lowercase       :							List[str]									=				restype_atomaa_to_atomaa[protein_aatype]
       lowercase       :							Any									=				restype_atomaa_mask[protein_aatype]

       lowercase       :							Union[str, Any]									=				residx_atomaa_mask
       lowercase       :							Optional[Any]									=				residx_atomaa_to_atomaa.long()

       # create the gather indices for mapping back
       lowercase       :							Tuple									=				restype_atomaa_to_atomaa[protein_aatype]
       lowercase       :							int									=				residx_atomaa_to_atomaa.long()

       # create the corresponding mask
       lowercase       :							str									=				torch.zeros([21, 37]	,					dtype=torch.floataa	,					device=protein["aatype"].device    )
       for restype, restype_letter in enumerate(rc.restypes    ):
              lowercase       :							Dict									=				rc.restype_atoa[restype_letter]
              lowercase       :							Tuple									=				rc.residue_atoms[restype_name]
              for atom_name in atom_names:
                     lowercase       :							List[Any]									=				rc.atom_order[atom_name]
                     lowercase       :							Any									=				1

       lowercase       :							Optional[Any]									=				restype_atomaa_mask[protein_aatype]
       lowercase       :							str									=				residx_atomaa_mask

       return protein


def     __magic_name__      ( __snake_case							:			Dict[str, torch.Tensor]    )					->							int:
       lowercase       :							List[str]									=				tree_map(lambda __snake_case    : torch.tensor(UpperCamelCase__	,					device=batch["aatype"].device    )	,					UpperCamelCase__	,					np.ndarray    )
       lowercase       :							Union[str, Any]									=				tensor_tree_map(lambda __snake_case    : np.array(UpperCamelCase__    )	,					make_atomaa_masks(UpperCamelCase__    )    )
       return out



'''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						:							int    							=						[
    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						:							List[Any]    							=						logging.getLogger()





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      argparse.ArgumentParser()
	parser.add_argument('''-f'''							)
	__UpperCAmelCase      					=      parser.parse_args()
	return args.f





def    lowerCAmelCase       (		UpperCamelCase__					: Union[str, Any]     ,       UpperCamelCase__					: Any="eval"							):


	"""simple docstring"""
	__UpperCAmelCase      					=      os.path.join(UpperCamelCase__     ,       f"""{split}_results.json"""							)
	if os.path.exists(UpperCamelCase__							):
		with open(UpperCamelCase__     ,       '''r'''							) as f:
			return json.load(UpperCamelCase__							)
	raise ValueError(f"""can't find {path}"""							)


__lowerCAmelCase						:							str    							=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class     A ( UpperCAmelCase ):





							def    snake_case__    (   self					:				List[Any]						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_flax_glue.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.7_5						)





							@slow
							def    snake_case__    (   self					:				Optional[Any]						)				->						Optional[int]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_clm_flax.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertLess(result['''eval_perplexity''']  ,		1_0_0						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						List[str]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_summarization_flax.main()
									__UpperCAmelCase      					=      get_results(__a  ,		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    snake_case__    (   self					:				Any						)				->						List[Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_mlm_flax.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertLess(result['''eval_perplexity''']  ,		4_2						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						str:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_ta_mlm_flax.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.4_2						)





							@slow
							def    snake_case__    (   self					:				Dict						)				->						Tuple:
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								__UpperCAmelCase      					=      7 if get_gpu_count() > 1 else 2

								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_flax_ner.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_accuracy''']  ,		0.7_5						)
									self.assertGreaterEqual(result['''eval_f1''']  ,		0.3						)






							@slow
							def    snake_case__    (   self					:				Optional[Any]						)				->						List[Any]:
								__UpperCAmelCase      					=      self.get_auto_remove_tmp_dir()
								__UpperCAmelCase      					=      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(__a  ,		'''argv'''  ,		__a						):
									run_qa.main()
									__UpperCAmelCase      					=      get_results(__a						)
									self.assertGreaterEqual(result['''eval_f1''']  ,		3_0						)
									self.assertGreaterEqual(result['''eval_exact''']  ,		3_0						)
