





"""simple docstring"""


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		__magic_name__					:				Dict	= """platform"""

		import jax
		import jax.numpy as jnp

		from transformers import AutoTokenizer
		from transformers.models.mbart.modeling_flax_mbart import (
		    FlaxMBartForConditionalGeneration,
		    FlaxMBartForQuestionAnswering,
		    FlaxMBartForSequenceClassification,
		    FlaxMBartModel,
		    shift_tokens_right,
		)

def     UpperCamelCase       (SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=None       ,    SCREAMING_SNAKE_CASE=None       ,    SCREAMING_SNAKE_CASE=None       ,    SCREAMING_SNAKE_CASE=None       ,    SCREAMING_SNAKE_CASE=None       ,    SCREAMING_SNAKE_CASE=None       ,    ):
						if attention_mask is None:
												UpperCamelCase							:		List[Any]							      = np.where(input_ids != config.pad_token_id       ,    1       ,    0    )
						if decoder_attention_mask is None:
												UpperCamelCase							:		str							      = np.where(decoder_input_ids != config.pad_token_id       ,    1       ,    0    )
						if head_mask is None:
												UpperCamelCase							:		Union[str, Any]							      = np.ones((config.encoder_layers, config.encoder_attention_heads)    )
						if decoder_head_mask is None:
												UpperCamelCase							:		Any							      = np.ones((config.decoder_layers, config.decoder_attention_heads)    )
						if cross_attn_head_mask is None:
												UpperCamelCase							:		Any							      = np.ones((config.decoder_layers, config.decoder_attention_heads)    )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						}






class        lowercase__ (      unittest.TestCase  ):

			"""simple docstring"""







			def __init__(			self	,      _A	,      _A=1_3	,      _A=7	,      _A=True	,      _A=False	,      _A=9_9	,      _A=1_6	,      _A=2	,      _A=4	,      _A=4	,      _A="gelu"	,      _A=0.1	,      _A=0.1	,      _A=3_2	,      _A=2	,      _A=1	,      _A=0	,      _A=2	,      _A=0.02	,      ):






									'''simple docstring'''







									UpperCamelCase							:		int							      = parent
									UpperCamelCase							:		Optional[int]							      = batch_size
									UpperCamelCase							:		Optional[Any]							      = seq_length
									UpperCamelCase							:		str							      = is_training
									UpperCamelCase							:		str							      = use_labels
									UpperCamelCase							:		int							      = vocab_size
									UpperCamelCase							:		List[Any]							      = hidden_size
									UpperCamelCase							:		List[str]							      = num_hidden_layers
									UpperCamelCase							:		Any							      = num_attention_heads
									UpperCamelCase							:		Optional[int]							      = intermediate_size
									UpperCamelCase							:		Tuple							      = hidden_act
									UpperCamelCase							:		Dict							      = hidden_dropout_prob
									UpperCamelCase							:		int							      = attention_probs_dropout_prob
									UpperCamelCase							:		Tuple							      = max_position_embeddings
									UpperCamelCase							:		List[Any]							      = eos_token_id
									UpperCamelCase							:		Optional[int]							      = pad_token_id
									UpperCamelCase							:		Tuple							      = bos_token_id
									UpperCamelCase							:		Tuple							      = decoder_start_token_id
									UpperCamelCase							:		Dict							      = initializer_range







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Union[str, Any]							      = np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,      self.vocab_size						)	,      3	,      self.vocab_size						)
									UpperCamelCase							:		Optional[int]							      = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,      dtype=np.intaa						))	,      -1						)

									UpperCamelCase							:		Union[str, Any]							      = shift_tokens_right(_A	,      1						)

									UpperCamelCase							:		int							      = MBartConfig(
									    vocab_size=self.vocab_size	,      d_model=self.hidden_size	,      encoder_layers=self.num_hidden_layers	,      decoder_layers=self.num_hidden_layers	,      encoder_attention_heads=self.num_attention_heads	,      decoder_attention_heads=self.num_attention_heads	,      encoder_ffn_dim=self.intermediate_size	,      decoder_ffn_dim=self.intermediate_size	,      dropout=self.hidden_dropout_prob	,      attention_dropout=self.attention_probs_dropout_prob	,      max_position_embeddings=self.max_position_embeddings	,      eos_token_id=self.eos_token_id	,      bos_token_id=self.bos_token_id	,      pad_token_id=self.pad_token_id	,      decoder_start_token_id=self.decoder_start_token_id	,      initializer_range=self.initializer_range	,      use_cache=_A	,      )
									UpperCamelCase							:		Union[str, Any]							      = prepare_mbart_inputs_dict(_A	,      _A	,      _A						)
									return config, inputs_dict







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		List[str]							      = self.prepare_config_and_inputs()
									return config, inputs_dict







			def      _a			(			self	,      _A	,      _A	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[int]							      = 2_0
									UpperCamelCase							:		int							      = model_class_name(_A						)

									UpperCamelCase							:		List[Any]							      = model.encode(inputs_dict["""input_ids"""]						)

									UpperCamelCase      ,	UpperCamelCase							:		List[Any]							      = (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									UpperCamelCase							:		str							      = model.init_cache(decoder_input_ids.shape[0]	,      _A	,      _A						)
									UpperCamelCase							:		Any							      = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,      dtype="""i4"""						)

									UpperCamelCase							:		List[Any]							      = jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]	,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,      )
									UpperCamelCase							:		Dict							      = model.decode(
									    decoder_input_ids[:, :-1]	,      _A	,      decoder_attention_mask=_A	,      past_key_values=_A	,      decoder_position_ids=_A	,      )

									UpperCamelCase							:		int							      = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,      dtype="""i4"""						)
									UpperCamelCase							:		Dict							      = model.decode(
									    decoder_input_ids[:, -1:]	,      _A	,      decoder_attention_mask=_A	,      past_key_values=outputs_cache.past_key_values	,      decoder_position_ids=_A	,      )

									UpperCamelCase							:		Optional[int]							      = model.decode(_A	,      _A						)

									UpperCamelCase							:		str							      = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
									self.parent.assertTrue(diff < 1e-3	,      msg=f"""Max diff is {diff}"""						)







			def      _a			(			self	,      _A	,      _A	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		List[str]							      = 2_0
									UpperCamelCase							:		Dict							      = model_class_name(_A						)

									UpperCamelCase							:		Optional[int]							      = model.encode(inputs_dict["""input_ids"""]						)

									UpperCamelCase      ,	UpperCamelCase							:		Optional[int]							      = (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									UpperCamelCase							:		Union[str, Any]							      = jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
									    ]	,      axis=-1	,      )

									UpperCamelCase							:		int							      = model.init_cache(decoder_input_ids.shape[0]	,      _A	,      _A						)
									UpperCamelCase							:		List[Any]							      = jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]	,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,      )

									UpperCamelCase							:		str							      = model.decode(
									    decoder_input_ids[:, :-1]	,      _A	,      decoder_attention_mask=_A	,      past_key_values=_A	,      decoder_position_ids=_A	,      )
									UpperCamelCase							:		Any							      = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,      dtype="""i4"""						)
									UpperCamelCase							:		List[str]							      = model.decode(
									    decoder_input_ids[:, -1:]	,      _A	,      past_key_values=outputs_cache.past_key_values	,      decoder_attention_mask=_A	,      decoder_position_ids=_A	,      )

									UpperCamelCase							:		int							      = model.decode(_A	,      _A	,      decoder_attention_mask=_A						)

									UpperCamelCase							:		str							      = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
									self.parent.assertTrue(diff < 1e-3	,      msg=f"""Max diff is {diff}"""						)






@require_flax
class        lowercase__ (      unittest.TestCase  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Tuple								=     99







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Dict							      = np.array(
									    [
									        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
									        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
									        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
									        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
									        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
									        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
									        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
									        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
									        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
									        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
									        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
									        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
									        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
									    ]	,      dtype=np.intaa	,      )

									UpperCamelCase							:		Union[str, Any]							      = input_ids.shape[0]
									UpperCamelCase							:		List[Any]							      = MBartConfig(
									    vocab_size=self.vocab_size	,      d_model=2_4	,      encoder_layers=2	,      decoder_layers=2	,      encoder_attention_heads=2	,      decoder_attention_heads=2	,      encoder_ffn_dim=3_2	,      decoder_ffn_dim=3_2	,      max_position_embeddings=4_8	,      eos_token_id=2	,      pad_token_id=1	,      bos_token_id=0	,      )
									return config, input_ids, batch_size







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase							:		Optional[Any]							      = self._get_config_and_data()
									UpperCamelCase							:		Union[str, Any]							      = FlaxMBartForSequenceClassification(_A						)
									UpperCamelCase							:		List[Any]							      = model(input_ids=_A	,      decoder_input_ids=_A						)
									UpperCamelCase							:		Any							      = (batch_size, config.num_labels)
									self.assertEqual(outputs["""logits"""].shape	,      _A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase							:		List[str]							      = self._get_config_and_data()
									UpperCamelCase							:		Union[str, Any]							      = FlaxMBartForQuestionAnswering(_A						)
									UpperCamelCase							:		List[str]							      = model(input_ids=_A						)

									self.assertEqual(outputs["""start_logits"""].shape	,      input_ids.shape						)
									self.assertEqual(outputs["""end_logits"""].shape	,      input_ids.shape						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase							:		Optional[int]							      = self._get_config_and_data()
									UpperCamelCase							:		str							      = FlaxMBartForConditionalGeneration(_A						)
									UpperCamelCase							:		Optional[Any]							      = lm_model(input_ids=_A						)
									UpperCamelCase							:		List[str]							      = (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs["""logits"""].shape	,      _A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Union[str, Any]							      = MBartConfig(
									    vocab_size=self.vocab_size	,      d_model=1_4	,      encoder_layers=2	,      decoder_layers=2	,      encoder_attention_heads=2	,      decoder_attention_heads=2	,      encoder_ffn_dim=8	,      decoder_ffn_dim=8	,      max_position_embeddings=4_8	,      )
									UpperCamelCase							:		List[str]							      = FlaxMBartForConditionalGeneration(_A						)
									UpperCamelCase							:		Tuple							      = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]	,      dtype=np.intaa						)
									UpperCamelCase							:		List[Any]							      = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]	,      dtype=np.intaa						)
									UpperCamelCase							:		str							      = lm_model(input_ids=_A	,      decoder_input_ids=_A						)
									UpperCamelCase							:		Optional[int]							      = (*summary.shape, config.vocab_size)
									self.assertEqual(outputs["""logits"""].shape	,      _A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]	,      dtype=np.intaa						)
									UpperCamelCase							:		List[Any]							      = shift_tokens_right(_A	,      1						)
									UpperCamelCase							:		Union[str, Any]							      = np.equal(_A	,      1						).astype(np.floataa						).sum()
									UpperCamelCase							:		str							      = np.equal(_A	,      1						).astype(np.floataa						).sum()
									self.assertEqual(shifted.shape	,      input_ids.shape						)
									self.assertEqual(_A	,      n_pad_before - 1						)
									self.assertTrue(np.equal(shifted[:, 0]	,      2						).all()						)






@require_flax
class        lowercase__ (      __SCREAMING_SNAKE_CASE				,    unittest.TestCase				,    __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Optional[int]								=     True
			__lowerCAmelCase    :							str								=     (
			    (
			        FlaxMBartModel,
			        FlaxMBartForConditionalGeneration,
			        FlaxMBartForSequenceClassification,
			        FlaxMBartForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			__lowerCAmelCase    :							List[str]								=     (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		str							      = FlaxMBartModelTester(self						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		List[str]							      = self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward(_A	,      _A	,      _A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		str							      = self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward_with_attn_mask(_A	,      _A	,      _A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		Union[str, Any]							      = self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__						):
																					UpperCamelCase							:		List[Any]							      = self._prepare_for_class(_A	,      _A						)
																					UpperCamelCase							:		List[Any]							      = model_class(_A						)

																					@jax.jit
																					def encode_jitted(_A	,      _A=None	,      **_A						):
																											return model.encode(input_ids=_A	,      attention_mask=_A						)

																					with self.subTest("""JIT Enabled"""						):
																											UpperCamelCase							:		List[str]							      = encode_jitted(**_A						).to_tuple()

																					with self.subTest("""JIT Disabled"""						):
																											with jax.disable_jit():
																																	UpperCamelCase							:		str							      = encode_jitted(**_A						).to_tuple()

																					self.assertEqual(len(_A						)	,      len(_A						)						)
																					for jitted_output, output in zip(_A	,      _A						):
																											self.assertEqual(jitted_output.shape	,      output.shape						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		Tuple							      = self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__						):
																					UpperCamelCase							:		Union[str, Any]							      = model_class(_A						)
																					UpperCamelCase							:		Tuple							      = model.encode(inputs_dict["""input_ids"""]	,      inputs_dict["""attention_mask"""]						)

																					UpperCamelCase							:		List[str]							      = {
																					    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																					    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																					    """encoder_outputs""": encoder_outputs,
																					}

																					@jax.jit
																					def decode_jitted(_A	,      _A	,      _A						):
																											return model.decode(
																											    decoder_input_ids=_A	,      decoder_attention_mask=_A	,      encoder_outputs=_A	,      )

																					with self.subTest("""JIT Enabled"""						):
																											UpperCamelCase							:		str							      = decode_jitted(**_A						).to_tuple()

																					with self.subTest("""JIT Disabled"""						):
																											with jax.disable_jit():
																																	UpperCamelCase							:		Dict							      = decode_jitted(**_A						).to_tuple()

																					self.assertEqual(len(_A						)	,      len(_A						)						)
																					for jitted_output, output in zip(_A	,      _A						):
																											self.assertEqual(jitted_output.shape	,      output.shape						)







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									for model_class_name in self.all_model_classes:
															UpperCamelCase							:		Dict							      = model_class_name.from_pretrained("""facebook/mbart-large-cc25"""	,      from_pt=_A						)
															# FlaxMBartForSequenceClassification expects eos token in input_ids
															UpperCamelCase							:		Optional[Any]							      = np.ones((1, 1)						) * model.config.eos_token_id
															UpperCamelCase							:		Any							      = model(_A						)
															self.assertIsNotNone(_A						)






@require_flax
@require_sentencepiece
@require_tokenizers
class        lowercase__ (      unittest.TestCase  ):

			"""simple docstring"""
			__lowerCAmelCase    :							List[str]								=     [
			    """ UN Chief Says There Is No Military Solution in Syria""",
			]
			__lowerCAmelCase    :							Tuple								=     [
			    """Şeful ONU declară că nu există o soluţie militară în Siria""",
			]
			__lowerCAmelCase    :							Tuple								=     """facebook/mbart-large-en-ro"""







			@cached_property
			def      _a			(			self						):






									'''simple docstring'''







									return AutoTokenizer.from_pretrained(self.model_name						)







			@cached_property
			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		str							      = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name	,      from_pt=_A						)
									return model







			def      _a			(			self	,      **_A						):






									'''simple docstring'''







									UpperCamelCase							:		List[Any]							      = self.translate_src_text(**_A						)
									self.assertListEqual(self.expected_text	,      _A						)







			def      _a			(			self	,      **_A						):






									'''simple docstring'''







									UpperCamelCase							:		List[str]							      = self.tokenizer(self.src_text	,      **_A	,      return_tensors="""np"""						)
									UpperCamelCase							:		Optional[int]							      = self.model.generate(
									    model_inputs.input_ids	,      attention_mask=model_inputs.attention_mask	,      decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]	,      early_stopping=_A	,      num_beams=2	,      ).sequences
									UpperCamelCase							:		List[str]							      = self.tokenizer.batch_decode(_A	,      skip_special_tokens=_A						)
									return generated_words







			@slow
			def      _a			(			self						):






									'''simple docstring'''







									self._assert_generated_batch_equal_expected()










"""simple docstring"""


from __future__ import annotations

def     UpperCamelCase       (SCREAMING_SNAKE_CASE    ):
						UpperCamelCase							:		Tuple							      = [0] * max_one

						for i in range(0       ,    SCREAMING_SNAKE_CASE    ):
												UpperCamelCase							:		Union[str, Any]							      = i

						for i in range(2       ,    SCREAMING_SNAKE_CASE    ):
												if totients[i] == i:
																		for j in range(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE    ):
																								totients[j] -= totients[j] // i

						return totients

def     UpperCamelCase       (SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE    ):
						return sorted(str(SCREAMING_SNAKE_CASE    )    ) == sorted(str(SCREAMING_SNAKE_CASE    )    )

def     UpperCamelCase       (SCREAMING_SNAKE_CASE = 1000_0000    ):
						UpperCamelCase							:		List[str]							      = 1  # i
						UpperCamelCase							:		Optional[int]							      = 0  # φ(i)
						UpperCamelCase							:		List[str]							      = get_totients(max_n + 1    )

						for i in range(2       ,    max_n + 1    ):
												UpperCamelCase							:		Tuple							      = totients[i]

												if i * min_denominator < min_numerator * t and has_same_digits(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE    ):
																		UpperCamelCase							:		str							      = i
																		UpperCamelCase							:		Tuple							      = t

						return min_numerator


if __name__ == "__main__":
		print(f'''{solution() = }''')



