
"""simple docstring"""
import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
lowercase		:    Tuple			=       {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      Tuple:


					'''simple docstring'''
					__a:    int		     =       search.split()
					__a:    str		     =       dict((g.split("""="""			) for g in groups)			)
					__a:    Optional[Any]		     =       list(entries.keys()			)
					__a:    Union[str, Any]		     =       [[f"""--{k} {v}""" for v in vs.split(""":"""			)] for k, vs in entries.items()]
					__a:    Optional[int]		     =       [list(lowerCamelCase_			) for x in itertools.product(*lowerCamelCase_			)]
					return matrix, entry_names


def      lowerCamelCase_     (						)  ->      str:


					'''simple docstring'''
					__a:    Union[str, Any]		     =       sys.argv[0]

					__a:    Optional[int]		     =       argparse.ArgumentParser(
					    usage=(
					        """\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"""
					        """ refer to `run_eval.py -h` for the complete list."""
					    )			)
					parser.add_argument(
					    """--search"""						,					type=lowerCamelCase_						,					required=lowerCamelCase_						,					help="""param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""""						,					)
					parser.add_argument(
					    """--bs"""						,					type=lowerCamelCase_						,					default=8						,					required=lowerCamelCase_						,					help="""initial batch size (may get reduced if it's too big)"""			)
					parser.add_argument("""--task"""						,					type=lowerCamelCase_						,					help="""used for task_specific_params + metrics"""			)
					parser.add_argument(
					    """--info"""						,					nargs="""?"""						,					type=lowerCamelCase_						,					const=datetime_now()						,					help=(
					        """add custom notes to be printed before the results table. If no value is passed, the current datetime"""
					        """ string will be used."""
					    )						,					)
					__a     ,					__a:    List[str]		     =       parser.parse_known_args()
					# we share some of the args
					args_main.extend(["""--task""", args.task]			)
					__a:    str		     =       [prog] + args_main

					# to support variations like translation_en_to_de"
					__a:    Optional[int]		     =       """translation""" if """translation""" in args.task else """summarization"""

					__a     ,					__a:    Any		     =       parse_search_arg(args.search			)
					__a:    List[Any]		     =       task_score_names[task]  # score cols first
					__a:    str		     =       {col: len(str(lowerCamelCase_			)			) for col in col_names}
					__a:    List[str]		     =       []
					for r in matrix:
										__a:    Dict		     =       dict((x.replace("""--"""						,					""""""			).split() for x in r)			)
										__a:    Union[str, Any]		     =       """ """.join(lowerCamelCase_			).split()
										args_exp.extend(["""--bs""", str(args.bs			)]			)  # in case we need to reduce its size due to CUDA OOM
										__a:    List[Any]		     =       args_normal + args_exp

										# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

										__a:    Dict		     =       run_generate(verbose=lowerCamelCase_			)
										# make sure scores are first in the table
										__a:    List[Any]		     =       OrderedDict()
										for score in task_score_names[task]:
															__a:    Tuple		     =       scores[score]
										result.update(lowerCamelCase_			)
										results.append(lowerCamelCase_			)

										# find widest entries
										for k, v in result.items():
															__a:    List[Any]		     =       len(str(lowerCamelCase_			)			)
															if l > col_widths[k]:
																				__a:    List[Any]		     =       l

					__a:    Optional[Any]		     =       sorted(lowerCamelCase_						,					key=operator.itemgetter(*task_score_names[task]			)						,					reverse=lowerCamelCase_			)
					print(""" | """.join([f"""{col:{col_widths[col]}}""" for col in col_names]			)			)
					print(""" | """.join([f"""{'-'*col_widths[col]}""" for col in col_names]			)			)
					for row in results_sorted:
										print(""" | """.join([f"""{row[col]:{col_widths[col]}}""" for col in col_names]			)			)

					__a:    int		     =       results_sorted[0]
					for score in task_score_names[task]:
										del best[score]
					__a:    Union[str, Any]		     =       [f"""--{k} {v}""" for k, v in best.items()]
					__a:    Any		     =       ["""--bs""", str(args.bs			)]
					if args.info:
										print(f"""\nInfo: {args.info}"""			)
					print("""\nBest score args:"""			)
					print(""" """.join(args_main + best_args + dyn_args			)			)

					return results_sorted


if __name__ == "__main__":
			# Usage:
			# [normal-run_eval_search.py cmd plus] \
			# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
			#
			# Example:
			# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
			# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
			# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
			# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
			run_search()


"""simple docstring"""
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch




class  _snake_case	(    unittest.TestCase							):






							@patch("""sys.argv"""    ,			["""fakeprogrampath""", """env"""]     )
							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												with CaptureStd() as cs:
																	transformers.commands.transformers_cli.main()
												self.assertIn("""Python version"""    ,			cs.out     )
												self.assertIn("""Platform"""    ,			cs.out     )
												self.assertIn("""Using distributed or parallel set-up in script?"""    ,			cs.out     )






							@is_pt_tf_cross_test
							@patch(
							    """sys.argv"""    ,			["""fakeprogrampath""", """pt-to-tf""", """--model-name""", """hf-internal-testing/tiny-random-gptj""", """--no-pr"""]     )
							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""    ,			ignore_errors=snake_case__     )  # cleans potential past runs
												transformers.commands.transformers_cli.main()

												# The original repo has no TF weights -- if they exist, they were created by the CLI
												self.assertTrue(os.path.exists("""/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"""     )     )






							@require_torch
							@patch("""sys.argv"""    ,			["""fakeprogrampath""", """download""", """hf-internal-testing/tiny-random-gptj""", """--cache-dir""", """/tmp"""]     )
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												# # remove any previously downloaded model to start clean
												shutil.rmtree("""/tmp/models--hf-internal-testing--tiny-random-gptj"""    ,			ignore_errors=snake_case__     )

												# run the command
												transformers.commands.transformers_cli.main()

												# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/refs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"""     )     )






							@require_torch
							@patch(
							    """sys.argv"""    ,			[
							        """fakeprogrampath""",
							        """download""",
							        """hf-internal-testing/test_dynamic_model_with_tokenizer""",
							        """--trust-remote-code""",
							        """--cache-dir""",
							        """/tmp""",
							    ]    ,			)
							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												# # remove any previously downloaded model to start clean
												shutil.rmtree("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"""    ,			ignore_errors=snake_case__     )

												# run the command
												transformers.commands.transformers_cli.main()

												# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"""     )     )
												self.assertTrue(
												    os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"""     )     )
