



"""simple docstring"""






import json
import sys


def 			_lowercase				(  __snake_case					,__snake_case	)						->   str:
			with open(__snake_case					,encoding="utf-8"	) as f:
						__lowerCAmelCase      :  Optional[int]												=       json.load(__snake_case	)

			__lowerCAmelCase      :  str												=       ["<details>", "<summary>Show updated benchmarks!</summary>", " "]

			for benchmark_name in sorted(__snake_case	):
						__lowerCAmelCase      :  Union[str, Any]												=       results[benchmark_name]

						__lowerCAmelCase      :  Optional[int]												=       benchmark_name.split("/"	)[-1]
						output_md.append(F"""### Benchmark: {benchmark_file_name}"""	)

						__lowerCAmelCase      :  str												=       "| metric |"
						__lowerCAmelCase      :  str												=       "|--------|"
						__lowerCAmelCase      :  Union[str, Any]												=       "| new / old (diff) |"
						for metric_name in sorted(__snake_case	):
									__lowerCAmelCase      :  Dict												=       benchmark_res[metric_name]
									__lowerCAmelCase      :  Any												=       metric_vals["new"]
									__lowerCAmelCase      :  List[Any]												=       metric_vals.get("old"					,__snake_case	)
									__lowerCAmelCase      :  Tuple												=       metric_vals.get("diff"					,__snake_case	)

									__lowerCAmelCase      :  List[Any]												=       F""" {new_val:f}""" if isinstance(__snake_case					,(int, float)	) else "None"

									if old_val is not None:
												val_str += F""" / {old_val:f}""" if isinstance(__snake_case					,(int, float)	) else "None"
									if dif_val is not None:
												val_str += F""" ({dif_val:f})""" if isinstance(__snake_case					,(int, float)	) else "None"

									title += " " + metric_name + " |"
									lines += "---|"
									value += val_str + " |"

						output_md += [title, lines, value, " "]

			output_md.append("</details>"	)

			with open(__snake_case					,"w"					,encoding="utf-8"	) as f:
						f.writelines("\n".join(__snake_case	)	)


if __name__ == "__main__":
	__snake_case							:   int      =    sys.argv[1]
	__snake_case							:   Dict      =    sys.argv[2]

	format_json_to_md(input_json_file, output_md_file)




"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)