





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
				__lowerCAmelCase 	=      "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      a    (   _lowercase :  Dict			,    _lowercase :  List[str]			,    _lowercase :  int=None			,    _lowercase :  int=None			,    _lowercase :  Any=None			,    _lowercase :  int=None			,    _lowercase :  Optional[Any]=None			,    _lowercase :  Optional[int]=None			,    )  ->Any:
		if attention_mask is None:
				UpperCamelCase__								=						np.where(input_ids != config.pad_token_id			,    1			,    0							)
		if decoder_attention_mask is None:
				UpperCamelCase__								=						np.where(decoder_input_ids != config.pad_token_id			,    1			,    0							)
		if head_mask is None:
				UpperCamelCase__								=						np.ones((config.encoder_layers, config.encoder_attention_heads)							)
		if decoder_head_mask is None:
				UpperCamelCase__								=						np.ones((config.decoder_layers, config.decoder_attention_heads)							)
		if cross_attn_head_mask is None:
				UpperCamelCase__								=						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 	_UpperCAmelCase						( unittest.TestCase       ):




					def __init__(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=13     ,   __lowerCAmelCase=7     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=99     ,   __lowerCAmelCase=16     ,   __lowerCAmelCase=2     ,   __lowerCAmelCase=4     ,   __lowerCAmelCase=4     ,   __lowerCAmelCase="gelu"     ,   __lowerCAmelCase=0.1     ,   __lowerCAmelCase=0.1     ,   __lowerCAmelCase=32     ,   __lowerCAmelCase=2     ,   __lowerCAmelCase=1     ,   __lowerCAmelCase=0     ,   __lowerCAmelCase=2     ,   __lowerCAmelCase=0.0_2     ,   )    ->	str:
							UpperCamelCase__								=						parent
							UpperCamelCase__								=						batch_size
							UpperCamelCase__								=						seq_length
							UpperCamelCase__								=						is_training
							UpperCamelCase__								=						use_labels
							UpperCamelCase__								=						vocab_size
							UpperCamelCase__								=						hidden_size
							UpperCamelCase__								=						num_hidden_layers
							UpperCamelCase__								=						num_attention_heads
							UpperCamelCase__								=						intermediate_size
							UpperCamelCase__								=						hidden_act
							UpperCamelCase__								=						hidden_dropout_prob
							UpperCamelCase__								=						attention_probs_dropout_prob
							UpperCamelCase__								=						max_position_embeddings
							UpperCamelCase__								=						eos_token_id
							UpperCamelCase__								=						pad_token_id
							UpperCamelCase__								=						bos_token_id
							UpperCamelCase__								=						decoder_start_token_id
							UpperCamelCase__								=						initializer_range




					def 						snake_case_		(    self							)    ->	int:
							UpperCamelCase__								=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,   self.vocab_size							)     ,   3     ,   self.vocab_size							)
							UpperCamelCase__								=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,   dtype=np.intaa							))     ,   -1							)

							UpperCamelCase__								=						shift_tokens_right(__lowerCAmelCase     ,   1							)

							UpperCamelCase__								=						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=__lowerCAmelCase     ,   )
							UpperCamelCase__								=						prepare_mbart_inputs_dict(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)
							return config, inputs_dict




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__	,      UpperCamelCase__								=						self.prepare_config_and_inputs()
							return config, inputs_dict




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	Optional[Any]:
							UpperCamelCase__								=						20
							UpperCamelCase__								=						model_class_name(__lowerCAmelCase							)

							UpperCamelCase__								=						model.encode(inputs_dict["""input_ids"""]							)

							UpperCamelCase__	,      UpperCamelCase__								=						(
							    inputs_dict["""decoder_input_ids"""],
							    inputs_dict["""decoder_attention_mask"""],
							)

							UpperCamelCase__								=						model.init_cache(decoder_input_ids.shape[0]     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)
							UpperCamelCase__								=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,   dtype="""i4"""							)

							UpperCamelCase__								=						jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]     ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,   )
							UpperCamelCase__								=						model.decode(
							    decoder_input_ids[:, :-1]     ,   __lowerCAmelCase     ,   decoder_attention_mask=__lowerCAmelCase     ,   past_key_values=__lowerCAmelCase     ,   decoder_position_ids=__lowerCAmelCase     ,   )

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

							UpperCamelCase__								=						model.decode(__lowerCAmelCase     ,   __lowerCAmelCase							)

							UpperCamelCase__								=						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 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	Optional[Any]:
							UpperCamelCase__								=						20
							UpperCamelCase__								=						model_class_name(__lowerCAmelCase							)

							UpperCamelCase__								=						model.encode(inputs_dict["""input_ids"""]							)

							UpperCamelCase__	,      UpperCamelCase__								=						(
							    inputs_dict["""decoder_input_ids"""],
							    inputs_dict["""decoder_attention_mask"""],
							)

							UpperCamelCase__								=						jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
							    ]     ,   axis=-1     ,   )

							UpperCamelCase__								=						model.init_cache(decoder_input_ids.shape[0]     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)
							UpperCamelCase__								=						jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]     ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,   )

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

							UpperCamelCase__								=						model.decode(__lowerCAmelCase     ,   __lowerCAmelCase     ,   decoder_attention_mask=__lowerCAmelCase							)

							UpperCamelCase__								=						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 	_UpperCAmelCase						( unittest.TestCase       ):
					__A        = 99




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							UpperCamelCase__								=						np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ]     ,   dtype=np.intaa     ,   )

							UpperCamelCase__								=						input_ids.shape[0]
							UpperCamelCase__								=						MBartConfig(
							    vocab_size=self.vocab_size     ,   d_model=24     ,   encoder_layers=2     ,   decoder_layers=2     ,   encoder_attention_heads=2     ,   decoder_attention_heads=2     ,   encoder_ffn_dim=32     ,   decoder_ffn_dim=32     ,   max_position_embeddings=48     ,   eos_token_id=2     ,   pad_token_id=1     ,   bos_token_id=0     ,   )
							return config, input_ids, batch_size




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						self._get_config_and_data()
							UpperCamelCase__								=						FlaxMBartForSequenceClassification(__lowerCAmelCase							)
							UpperCamelCase__								=						model(input_ids=__lowerCAmelCase     ,   decoder_input_ids=__lowerCAmelCase							)
							UpperCamelCase__								=						(batch_size, config.num_labels)
							self.assertEqual(outputs["""logits"""].shape     ,   __lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						self._get_config_and_data()
							UpperCamelCase__								=						FlaxMBartForQuestionAnswering(__lowerCAmelCase							)
							UpperCamelCase__								=						model(input_ids=__lowerCAmelCase							)

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




					def 						snake_case_		(    self							)    ->	Optional[int]:
							UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						self._get_config_and_data()
							UpperCamelCase__								=						FlaxMBartForConditionalGeneration(__lowerCAmelCase							)
							UpperCamelCase__								=						lm_model(input_ids=__lowerCAmelCase							)
							UpperCamelCase__								=						(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape     ,   __lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						MBartConfig(
							    vocab_size=self.vocab_size     ,   d_model=14     ,   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=48     ,   )
							UpperCamelCase__								=						FlaxMBartForConditionalGeneration(__lowerCAmelCase							)
							UpperCamelCase__								=						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,   dtype=np.intaa							)
							UpperCamelCase__								=						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,   dtype=np.intaa							)
							UpperCamelCase__								=						lm_model(input_ids=__lowerCAmelCase     ,   decoder_input_ids=__lowerCAmelCase							)
							UpperCamelCase__								=						(*summary.shape, config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape     ,   __lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,   dtype=np.intaa							)
							UpperCamelCase__								=						shift_tokens_right(__lowerCAmelCase     ,   1							)
							UpperCamelCase__								=						np.equal(__lowerCAmelCase     ,   1							).astype(np.floataa							).sum()
							UpperCamelCase__								=						np.equal(__lowerCAmelCase     ,   1							).astype(np.floataa							).sum()
							self.assertEqual(shifted.shape     ,   input_ids.shape							)
							self.assertEqual(__lowerCAmelCase     ,   n_pad_before - 1							)
							self.assertTrue(np.equal(shifted[:, 0]     ,   2							).all()							)




@require_flax
class 	_UpperCAmelCase						( _lowercase			,							unittest.TestCase			,							_lowercase       ):
					__A        = True
					__A        = (
					    (
					        FlaxMBartModel,
					        FlaxMBartForConditionalGeneration,
					        FlaxMBartForSequenceClassification,
					        FlaxMBartForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					__A        = (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						FlaxMBartModelTester(self							)




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__	,      UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__	,      UpperCamelCase__								=						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(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Tuple:
							UpperCamelCase__	,      UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
											UpperCamelCase__								=						self._prepare_for_class(__lowerCAmelCase     ,   __lowerCAmelCase							)
											UpperCamelCase__								=						model_class(__lowerCAmelCase							)

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

											with self.subTest("""JIT Enabled"""							):
													UpperCamelCase__								=						encode_jitted(**__lowerCAmelCase							).to_tuple()

											with self.subTest("""JIT Disabled"""							):
													with jax.disable_jit():
															UpperCamelCase__								=						encode_jitted(**__lowerCAmelCase							).to_tuple()

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




					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__	,      UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
											UpperCamelCase__								=						model_class(__lowerCAmelCase							)
											UpperCamelCase__								=						model.encode(inputs_dict["""input_ids"""]     ,   inputs_dict["""attention_mask"""]							)

											UpperCamelCase__								=						{
											    """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(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							):
													return model.decode(
													    decoder_input_ids=__lowerCAmelCase     ,   decoder_attention_mask=__lowerCAmelCase     ,   encoder_outputs=__lowerCAmelCase     ,   )

											with self.subTest("""JIT Enabled"""							):
													UpperCamelCase__								=						decode_jitted(**__lowerCAmelCase							).to_tuple()

											with self.subTest("""JIT Disabled"""							):
													with jax.disable_jit():
															UpperCamelCase__								=						decode_jitted(**__lowerCAmelCase							).to_tuple()

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




					@slow
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							for model_class_name in self.all_model_classes:
									UpperCamelCase__								=						model_class_name.from_pretrained("""facebook/mbart-large-cc25"""     ,   from_pt=__lowerCAmelCase							)
									# FlaxMBartForSequenceClassification expects eos token in input_ids
									UpperCamelCase__								=						np.ones((1, 1)							) * model.config.eos_token_id
									UpperCamelCase__								=						model(__lowerCAmelCase							)
									self.assertIsNotNone(__lowerCAmelCase							)




@require_flax
@require_sentencepiece
@require_tokenizers
class 	_UpperCAmelCase						( unittest.TestCase       ):
					__A        = [
					    " UN Chief Says There Is No Military Solution in Syria",
					]
					__A        = [
					    "Şeful ONU declară că nu există o soluţie militară în Siria",
					]
					__A        = "facebook/mbart-large-en-ro"




					@cached_property
					def 						snake_case_		(    self							)    ->	int:
							return AutoTokenizer.from_pretrained(self.model_name							)




					@cached_property
					def 						snake_case_		(    self							)    ->	Optional[int]:
							UpperCamelCase__								=						FlaxMBartForConditionalGeneration.from_pretrained(self.model_name     ,   from_pt=__lowerCAmelCase							)
							return model




					def 						snake_case_		(    self     ,   **__lowerCAmelCase							)    ->	Union[str, Any]:
							UpperCamelCase__								=						self.translate_src_text(**__lowerCAmelCase							)
							self.assertListEqual(self.expected_text     ,   __lowerCAmelCase							)




					def 						snake_case_		(    self     ,   **__lowerCAmelCase							)    ->	Dict:
							UpperCamelCase__								=						self.tokenizer(self.src_text     ,   **__lowerCAmelCase     ,   return_tensors="""np"""							)
							UpperCamelCase__								=						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=__lowerCAmelCase     ,   num_beams=2     ,   ).sequences
							UpperCamelCase__								=						self.tokenizer.batch_decode(__lowerCAmelCase     ,   skip_special_tokens=__lowerCAmelCase							)
							return generated_words




					@slow
					def 						snake_case_		(    self							)    ->	List[Any]:
							self._assert_generated_batch_equal_expected()












from __future__ import annotations

def      a    (   _lowercase :  float | int			,    _lowercase :  float | int			,    _lowercase :  float | int			,    )  ->list[float | int]:
		if not all((nth_term, start_term_a, common_ratio_r)							):
				return []
		UpperCamelCase__								=						[]
		UpperCamelCase__								=						1
		UpperCamelCase__								=						common_ratio_r
		for _ in range(int(_lowercase							)							):
				if not series:
						series.append(_lowercase							)
				else:
						power += 1
						series.append(float(start_term_a * multiple							)							)
						UpperCamelCase__								=						pow(float(_lowercase							)			,    _lowercase							)
		return series


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__lowerCAmelCase 	=      float(input("Enter the last number (n term) of the Geometric Series"))
				__lowerCAmelCase 	=      float(input("Enter the starting term (a) of the Geometric Series"))
				__lowerCAmelCase 	=      float(
				    input("Enter the common ratio between two terms (r) of the Geometric Series")
				)
				print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
				print(geometric_series(nth_term, start_term_a, common_ratio_r))





