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 	__lowerCamelCase							(				__a    : Tuple      ,							__a    : str      ,							__a    : List[Any]=None      ,							__a    : Any=None      ,							__a    : Optional[int]=None      ,							__a    : Union[str, Any]=None      ,							__a    : Any=None      ,							__a    : Any=None      ,							)      ->       List[Any]:
	if attention_mask is None:
		_lowercase   =np.where(input_ids != config.pad_token_id      ,							1      ,							0		)
	if decoder_attention_mask is None:
		_lowercase   =np.where(decoder_input_ids != config.pad_token_id      ,							1      ,							0		)
	if head_mask is None:
		_lowercase   =np.ones((config.encoder_layers, config.encoder_attention_heads)		)
	if decoder_head_mask is None:
		_lowercase   =np.ones((config.decoder_layers, config.decoder_attention_heads)		)
	if cross_attn_head_mask is None:
		_lowercase   =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 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	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       ,  ):
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =seq_length
		_lowercase   =is_training
		_lowercase   =use_labels
		_lowercase   =vocab_size
		_lowercase   =hidden_size
		_lowercase   =num_hidden_layers
		_lowercase   =num_attention_heads
		_lowercase   =intermediate_size
		_lowercase   =hidden_act
		_lowercase   =hidden_dropout_prob
		_lowercase   =attention_probs_dropout_prob
		_lowercase   =max_position_embeddings
		_lowercase   =eos_token_id
		_lowercase   =pad_token_id
		_lowercase   =bos_token_id
		_lowercase   =decoder_start_token_id
		_lowercase   =initializer_range


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

		_lowercase   =shift_tokens_right(lowerCAmelCase_       ,  1       )

		_lowercase   =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_       ,  )
		_lowercase   =prepare_mbart_inputs_dict(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		return config, inputs_dict


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.prepare_config_and_inputs()
		return config, inputs_dict


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =20
		_lowercase   =model_class_name(lowerCAmelCase_       )

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

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

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

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

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

		_lowercase   =model.decode(lowerCAmelCase_       ,  lowerCAmelCase_       )

		_lowercase   =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 __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =20
		_lowercase   =model_class_name(lowerCAmelCase_       )

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

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

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

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

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

		_lowercase   =model.decode(lowerCAmelCase_       ,  lowerCAmelCase_       ,  decoder_attention_mask=lowerCAmelCase_       )

		_lowercase   =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 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					99


	def __lowerCAmelCase      (   self       ):
		_lowercase   =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       ,  )

		_lowercase   =input_ids.shape[0]
		_lowercase   =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 __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   ,  _lowercase   =self._get_config_and_data()
		_lowercase   =FlaxMBartForSequenceClassification(lowerCAmelCase_       )
		_lowercase   =model(input_ids=lowerCAmelCase_       ,  decoder_input_ids=lowerCAmelCase_       )
		_lowercase   =(batch_size, config.num_labels)
		self.assertEqual(outputs["logits"].shape       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   ,  _lowercase   =self._get_config_and_data()
		_lowercase   =FlaxMBartForQuestionAnswering(lowerCAmelCase_       )
		_lowercase   =model(input_ids=lowerCAmelCase_       )

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   ,  _lowercase   =self._get_config_and_data()
		_lowercase   =FlaxMBartForConditionalGeneration(lowerCAmelCase_       )
		_lowercase   =lm_model(input_ids=lowerCAmelCase_       )
		_lowercase   =(batch_size, input_ids.shape[1], config.vocab_size)
		self.assertEqual(outputs["logits"].shape       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =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       ,  )
		_lowercase   =FlaxMBartForConditionalGeneration(lowerCAmelCase_       )
		_lowercase   =np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,  dtype=np.intaa       )
		_lowercase   =np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,  dtype=np.intaa       )
		_lowercase   =lm_model(input_ids=lowerCAmelCase_       ,  decoder_input_ids=lowerCAmelCase_       )
		_lowercase   =(*summary.shape, config.vocab_size)
		self.assertEqual(outputs["logits"].shape       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,  dtype=np.intaa       )
		_lowercase   =shift_tokens_right(lowerCAmelCase_       ,  1       )
		_lowercase   =np.equal(lowerCAmelCase_       ,  1       ).astype(np.floataa       ).sum()
		_lowercase   =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 	_a  (							lowerCamelCase_							,		unittest.TestCase							,		lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					True
	__SCREAMING_SNAKE_CASE              =					(
	    (
	        FlaxMBartModel,
	        FlaxMBartForConditionalGeneration,
	        FlaxMBartForSequenceClassification,
	        FlaxMBartForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE              =					(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =FlaxMBartModelTester(self       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =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 __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =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 __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__       ):
				_lowercase   =self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )
				_lowercase   =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"       ):
					_lowercase   =encode_jitted(**lowerCAmelCase_       ).to_tuple()

				with self.subTest("JIT Disabled"       ):
					with jax.disable_jit():
						_lowercase   =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 __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

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

				_lowercase   ={
				    "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"       ):
					_lowercase   =decode_jitted(**lowerCAmelCase_       ).to_tuple()

				with self.subTest("JIT Disabled"       ):
					with jax.disable_jit():
						_lowercase   =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 __lowerCAmelCase      (   self       ):
		for model_class_name in self.all_model_classes:
			_lowercase   =model_class_name.from_pretrained("facebook/mbart-large-cc25"       ,  from_pt=lowerCAmelCase_       )
			# FlaxMBartForSequenceClassification expects eos token in input_ids
			_lowercase   =np.ones((1, 1)       ) * model.config.eos_token_id
			_lowercase   =model(lowerCAmelCase_       )
			self.assertIsNotNone(lowerCAmelCase_       )

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



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					[
	    ' UN Chief Says There Is No Military Solution in Syria',
	]
	__SCREAMING_SNAKE_CASE              =					[
	    'Şeful ONU declară că nu există o soluţie militară în Siria',
	]
	__SCREAMING_SNAKE_CASE              =					'facebook/mbart-large-en-ro'


	@cached_property
	def __lowerCAmelCase      (   self       ):
		return AutoTokenizer.from_pretrained(self.model_name       )


	@cached_property
	def __lowerCAmelCase      (   self       ):
		_lowercase   =FlaxMBartForConditionalGeneration.from_pretrained(self.model_name       ,  from_pt=lowerCAmelCase_       )
		return model


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		_lowercase   =self.translate_src_text(**lowerCAmelCase_       )
		self.assertListEqual(self.expected_text       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		_lowercase   =self.tokenizer(self.src_text       ,  **lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =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
		_lowercase   =self.tokenizer.batch_decode(lowerCAmelCase_       ,  skip_special_tokens=lowerCAmelCase_       )
		return generated_words


	@slow
	def __lowerCAmelCase      (   self       ):
		self._assert_generated_batch_equal_expected()









"""simple docstring"""
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


__a           =							"Update an existing config file with the latest defaults while maintaining the old configuration."
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:int		=     args.config_file
						if config_file is None and Path(_lowercase      ).exists():
												snake_case_					:str		=     default_config_file
						elif not Path(_lowercase      ).exists():
												raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""      )
						snake_case_					:str		=     load_config_from_file(_lowercase      )

						if config_file.endswith(""".json"""      ):
												config.to_json_file(_lowercase      )
						else:
												config.to_yaml_file(_lowercase      )
						return config_file
def   A_    (     _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Any		=     parser.add_parser("""update""",   parents=_lowercase,   help=_lowercase,   formatter_class=_lowercase      )
						parser.add_argument(
						    """--config_file""",   default=_lowercase,   help=(
						        """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
						        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
						        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
						        """with 'huggingface'."""
						    ),   )

						parser.set_defaults(func=_lowercase      )
						return parser

def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:str		=     update_config(_lowercase      )
						print(f"""Sucessfully updated the configuration file at {config_file}."""      )
