






from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel





@require_tf
class __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				MarianConfig
	UpperCamelCase__								=				{}
	UpperCamelCase__								=				"gelu"

	def __init__(							self	,   UpperCAmelCase	,   UpperCAmelCase=13	,   UpperCAmelCase=7	,   UpperCAmelCase=True	,   UpperCAmelCase=False	,   UpperCAmelCase=99	,   UpperCAmelCase=32	,   UpperCAmelCase=2	,   UpperCAmelCase=4	,   UpperCAmelCase=37	,   UpperCAmelCase=0.1	,   UpperCAmelCase=0.1	,   UpperCAmelCase=20	,   UpperCAmelCase=2	,   UpperCAmelCase=1	,   UpperCAmelCase=0	,   ):
							"""simple docstring"""







							_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_dropout_prob
							_UpperCAmelCase     =      attention_probs_dropout_prob
							_UpperCAmelCase     =      max_position_embeddings
							_UpperCAmelCase     =      eos_token_id
							_UpperCAmelCase     =      pad_token_id
							_UpperCAmelCase     =      bos_token_id

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      ids_tensor([self.batch_size, self.seq_length - 1]	,   self.vocab_size   )
							_UpperCAmelCase     =      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )	,   1   )
							_UpperCAmelCase     =      tf.concat([input_ids, eos_tensor]	,   axis=1   )

							_UpperCAmelCase     =      ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size   )

							_UpperCAmelCase     =      self.config_cls(
							    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_ids=[2]	,   bos_token_id=self.bos_token_id	,   pad_token_id=self.pad_token_id	,   decoder_start_token_id=self.pad_token_id	,   **self.config_updates	,   )
							_UpperCAmelCase     =      prepare_marian_inputs_dict(UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   )
							return config, inputs_dict

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      TFMarianModel(config=UpperCAmelCase   ).get_decoder()
							_UpperCAmelCase     =      inputs_dict['input_ids']

							_UpperCAmelCase     =      input_ids[:1, :]
							_UpperCAmelCase     =      inputs_dict['attention_mask'][:1, :]
							_UpperCAmelCase     =      inputs_dict['head_mask']
							_UpperCAmelCase     =      1

							# first forward pass
							_UpperCAmelCase     =      model(UpperCAmelCase	,   attention_mask=UpperCAmelCase	,   head_mask=UpperCAmelCase	,   use_cache=UpperCAmelCase   )

							_UpperCAmelCase ,			_UpperCAmelCase     =      outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							_UpperCAmelCase     =      ids_tensor((self.batch_size, 3)	,   config.vocab_size   )
							_UpperCAmelCase     =      tf.cast(ids_tensor((self.batch_size, 3)	,   2   )	,   tf.inta   )

							# append to next input_ids and
							_UpperCAmelCase     =      tf.concat([input_ids, next_tokens]	,   axis=-1   )
							_UpperCAmelCase     =      tf.concat([attention_mask, next_attn_mask]	,   axis=-1   )

							_UpperCAmelCase     =      model(UpperCAmelCase	,   attention_mask=UpperCAmelCase   )[0]
							_UpperCAmelCase     =      model(UpperCAmelCase	,   attention_mask=UpperCAmelCase	,   past_key_values=UpperCAmelCase   )[0]

							self.parent.assertEqual(next_tokens.shape[1]	,   output_from_past.shape[1]   )

							# select random slice
							_UpperCAmelCase     =      int(ids_tensor((1,)	,   output_from_past.shape[-1]   )   )
							_UpperCAmelCase     =      output_from_no_past[:, -3:, random_slice_idx]
							_UpperCAmelCase     =      output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(UpperCAmelCase	,   UpperCAmelCase	,   rtol=1e-3   )



def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase=None			,		__lowerCAmelCase=None			,		__lowerCAmelCase=None			,		__lowerCAmelCase=None			,		__lowerCAmelCase=None			,		)->  List[Any]:





						"""simple docstring"""



						if attention_mask is None:
												_UpperCAmelCase     =      tf.cast(tf.math.not_equal(__lowerCAmelCase			,		config.pad_token_id     )			,		tf.inta     )
						if decoder_attention_mask is None:
												_UpperCAmelCase     =      tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape			,		dtype=tf.inta     ),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,		config.pad_token_id     )			,		tf.inta     ),
												    ]			,		axis=-1			,		)
						if head_mask is None:
												_UpperCAmelCase     =      tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
						if decoder_head_mask is None:
												_UpperCAmelCase     =      tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
						if cross_attn_head_mask is None:
												_UpperCAmelCase     =      tf.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,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}





@require_tf
class __lowerCamelCase			(				snake_case__					,	snake_case__					,	unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
	UpperCamelCase__								=				(TFMarianMTModel,) if is_tf_available() else ()
	UpperCamelCase__								=				(
	    {
	        "conversational": TFMarianMTModel,
	        "feature-extraction": TFMarianModel,
	        "summarization": TFMarianMTModel,
	        "text2text-generation": TFMarianMTModel,
	        "translation": TFMarianMTModel,
	    }
	    if is_tf_available()
	    else {}
	)
	UpperCamelCase__								=				True
	UpperCamelCase__								=				False
	UpperCamelCase__								=				False

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      TFMarianModelTester(self   )
							_UpperCAmelCase     =      ConfigTester(self	,   config_class=UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							self.config_tester.run_common_tests()

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase   )





@require_tf
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				1000  # show more chars for failing integration tests

	@classmethod
	def 	UpperCamelCase   (							cls   ):
							"""simple docstring"""







							_UpperCAmelCase     =      F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
							return cls

	@cached_property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return AutoTokenizer.from_pretrained(self.model_name   )

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return self.tokenizer.eos_token_id

	@cached_property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							warnings.simplefilter('error'   )
							_UpperCAmelCase     =      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
							assert isinstance(UpperCAmelCase	,   UpperCAmelCase   )
							_UpperCAmelCase     =      model.config
							self.assertListEqual(c.bad_words_ids	,   [[c.pad_token_id]]   )
							self.assertEqual(c.max_length	,   512   )
							self.assertEqual(c.decoder_start_token_id	,   c.pad_token_id   )
							return model

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.translate_src_text(**UpperCAmelCase   )
							self.assertListEqual(self.expected_text	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self	,   **UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.tokenizer(self.src_text	,   **UpperCAmelCase	,   padding=UpperCAmelCase	,   return_tensors='tf'   )
							_UpperCAmelCase     =      self.model.generate(
							    model_inputs.input_ids	,   attention_mask=model_inputs.attention_mask	,   num_beams=2	,   max_length=128   )
							_UpperCAmelCase     =      self.tokenizer.batch_decode(generated_ids.numpy()	,   skip_special_tokens=UpperCAmelCase   )
							return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				"mt"
	UpperCamelCase__								=				"en"
	UpperCamelCase__								=				["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
	UpperCamelCase__								=				["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]

	@unittest.skip('Skipping until #12647 is resolved.'   )
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				"en"
	UpperCamelCase__								=				"zh"
	UpperCamelCase__								=				["My name is Wolfgang and I live in Berlin"]
	UpperCamelCase__								=				["我叫沃尔夫冈 我住在柏林"]

	@unittest.skip('Skipping until #12647 is resolved.'   )
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				"en"
	UpperCamelCase__								=				"ROMANCE"
	UpperCamelCase__								=				[
	    ">>fr<< Don't spend so much time watching TV.",
	    ">>pt<< Your message has been sent.",
	    ">>es<< He's two years older than me.",
	]
	UpperCamelCase__								=				[
	    "Ne passez pas autant de temps à regarder la télé.",
	    "A sua mensagem foi enviada.",
	    "Es dos años más viejo que yo.",
	]

	@unittest.skip('Skipping until #12647 is resolved.'   )
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							self._assert_generated_batch_equal_expected()

	@unittest.skip('Skipping until #12647 is resolved.'   )
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      TranslationPipeline(self.model	,   self.tokenizer	,   framework='tf'   )
							_UpperCAmelCase     =      pipeline(self.src_text   )
							self.assertEqual(self.expected_text	,   [x['translation_text'] for x in output]   )








from __future__ import annotations

_a				     =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class __lowerCamelCase			:





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      -1
							_UpperCAmelCase     =      -1
							_UpperCAmelCase     =      [-1] * length
							_UpperCAmelCase     =      None
							_UpperCAmelCase     =      None

	def __repr__(							self   ):
							"""simple docstring"""







							return F"""Node(min_value={self.minn} max_value={self.maxx})"""



def __A   (				__lowerCAmelCase     )->  Node | None:





						"""simple docstring"""



						_UpperCAmelCase     =      Node(len(__lowerCAmelCase     )     )
						_UpperCAmelCase ,			_UpperCAmelCase     =      min(__lowerCAmelCase     ), max(__lowerCAmelCase     )
						# Leaf node case where the node contains only one unique value
						if root.minn == root.maxx:
												return root
						_UpperCAmelCase     =      (root.minn + root.maxx) // 2

						_UpperCAmelCase     =      []
						_UpperCAmelCase     =      []

						for index, num in enumerate(__lowerCAmelCase     ):
												if num <= pivot:
																		left_arr.append(__lowerCAmelCase     )
												else:
																		right_arr.append(__lowerCAmelCase     )
												_UpperCAmelCase     =      len(__lowerCAmelCase     )
						_UpperCAmelCase     =      build_tree(__lowerCAmelCase     )
						_UpperCAmelCase     =      build_tree(__lowerCAmelCase     )
						return root


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if index < 0 or node is None:
												return 0
						# Leaf node cases
						if node.minn == node.maxx:
												return index + 1 if node.minn == num else 0
						_UpperCAmelCase     =      (node.minn + node.maxx) // 2
						if num <= pivot:
												# go the left subtree and map index to the left subtree
												return rank_till_index(node.left			,		__lowerCAmelCase			,		node.map_left[index] - 1     )
						else:
												# go to the right subtree and map index to the right subtree
												return rank_till_index(node.right			,		__lowerCAmelCase			,		index - node.map_left[index]     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if start > end:
												return 0
						_UpperCAmelCase     =      rank_till_index(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						_UpperCAmelCase     =      rank_till_index(__lowerCAmelCase			,		__lowerCAmelCase			,		start - 1     )
						return rank_till_end - rank_before_start


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if index > (end - start) or start > end or node is None:
												return -1
						# Leaf node case
						if node.minn == node.maxx:
												return node.minn
						# Number of elements in the left subtree in interval [start, end]
						_UpperCAmelCase     =      node.map_left[end] - (
						    node.map_left[start - 1] if start else 0
						)
						if num_elements_in_left_tree > index:
												return quantile(
												    node.left			,		__lowerCAmelCase			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		)
						else:
												return quantile(
												    node.right			,		index - num_elements_in_left_tree			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		)


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if (
						    start > end
						    or node is None
						    or start_num > end_num
						    or node.minn > end_num
						    or node.maxx < start_num
						):
												return 0
						if start_num <= node.minn and node.maxx <= end_num:
												return end - start + 1
						_UpperCAmelCase     =      range_counting(
						    node.left			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)
						_UpperCAmelCase     =      range_counting(
						    node.right			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)
						return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()
