

from argparse import ArgumentParser

from .env import EnvironmentCommand
def 		UpperCamelCase_(     )			->					Any:
 UpperCAmelCase__     							=							ArgumentParser('Diffusers CLI tool'    ,       usage='diffusers-cli <command> [<args>]'  )
 UpperCAmelCase__     							=							parser.add_subparsers(help='diffusers-cli command helpers'  )

 # Register commands
 EnvironmentCommand.register_subcommand(snake_case__  )

 # Let's go
 UpperCAmelCase__     							=							parser.parse_args()

 if not hasattr(snake_case__    ,       'func'  ):
  parser.print_help()
  exit(1  )

 # Run
 UpperCAmelCase__     							=							args.func(snake_case__  )
 service.run()


if __name__ == "__main__":
 main()









'''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       ):







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


														"""simple docstring"""


														_a      	=							FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
														_a      	=							AutoTokenizer.from_pretrained('''google/mt5-small'''   )

														_a      	=							tokenizer('''Hello there'''				,     return_tensors='''np'''   ).input_ids
														_a      	=							tokenizer('''Hi I am'''				,     return_tensors='''np'''   ).input_ids

														_a      	=							shift_tokens_right(lowerCAmelCase_				,     model.config.pad_token_id				,     model.config.decoder_start_token_id   )

														_a      	=							model(lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_   ).logits
														_a      	=							optax.softmax_cross_entropy(lowerCAmelCase_				,     onehot(lowerCAmelCase_				,     logits.shape[-1]   )   ).mean()

														_a      	=							-(labels.shape[-1] * loss.item())

														_a      	=							-8_4.9_1_2_7
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )

