





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)


def 		__a(SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   Optional[int]=False						,    SCREAMING_SNAKE_CASE_					:   Tuple=False						,    SCREAMING_SNAKE_CASE_					:   Optional[int]="data2vec."      ):



    '''simple docstring'''





    _lowerCAmelCase		     = 'backbone.' if is_semantic else ''

    _lowerCAmelCase		     = []
    for i in range(config.num_hidden_layers      ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')      )
        rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')      )
        rename_keys.append(
            (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')      )
        rename_keys.append(
            (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')      )
        rename_keys.append(
            (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')      )
        rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')      )
        rename_keys.append(
            (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')      )
        rename_keys.append(
            (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')      )
        rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')      )
        rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')      )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
            (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
            (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
        ]      )

    if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("mask_token", F'''{hf_prefix}embeddings.mask_token'''),
                (
                    "rel_pos_bias.relative_position_bias_table",
                    F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
                ),
                (
                    "rel_pos_bias.relative_position_index",
                    F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
                ),
                ("norm.weight", "layernorm.weight"),
                ("norm.bias", "layernorm.bias"),
            ]      )
    elif is_semantic:
        # semantic segmentation classification heads
        rename_keys.extend(
            [
                ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
                ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
                ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
                ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
            ]      )
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("fc_norm.weight", F'''{hf_prefix}pooler.layernorm.weight'''),
                ("fc_norm.bias", F'''{hf_prefix}pooler.layernorm.bias'''),
                ("head.weight", "classifier.weight"),
                ("head.bias", "classifier.bias"),
            ]      )

    return rename_keys


def 		__a(SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   List[str]=False						,    SCREAMING_SNAKE_CASE_					:   Any=False						,    SCREAMING_SNAKE_CASE_					:   str="data2vec_vision."      ):



    '''simple docstring'''





    for i in range(config.num_hidden_layers      ):
        _lowerCAmelCase		     = 'backbone.' if is_semantic else ''
        # queries, keys and values
        _lowerCAmelCase		     = state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''      )
        _lowerCAmelCase		     = state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''      )
        _lowerCAmelCase		     = state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''      )

        _lowerCAmelCase		     = in_proj_weight[
            : config.hidden_size, :
        ]
        _lowerCAmelCase		     = q_bias
        _lowerCAmelCase		     = in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        _lowerCAmelCase		     = in_proj_weight[
            -config.hidden_size :, :
        ]
        _lowerCAmelCase		     = v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        _lowerCAmelCase		     = state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''      )
        _lowerCAmelCase		     = state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''      )

        _lowerCAmelCase		     = gamma_a
        _lowerCAmelCase		     = gamma_a

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            _lowerCAmelCase		     = state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''      )
            _lowerCAmelCase		     = state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''      )

            _lowerCAmelCase		     = table
            _lowerCAmelCase		     = index


def 		__a():



    '''simple docstring'''





    _lowerCAmelCase		     = argparse.ArgumentParser(
        "Convert Data2VecVision to HF for image classification and pretraining"						,    add_help=SCREAMING_SNAKE_CASE_      )
    parser.add_argument("--hf_checkpoint_name"						,    type=SCREAMING_SNAKE_CASE_      )
    parser.add_argument("--input_size"						,    default=224						,    type=SCREAMING_SNAKE_CASE_						,    help="images input size"      )
    parser.add_argument("--beit_checkpoint"						,    default=""						,    help="beit checkpoint"      )

    return parser.parse_args()


def 		__a(SCREAMING_SNAKE_CASE_					:   Optional[Any]						,    SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]      ):



    '''simple docstring'''





    def load_state_dict(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]=""						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]="relative_position_index"      ):
        _lowerCAmelCase		     = []
        _lowerCAmelCase		     = []
        _lowerCAmelCase		     = []
        # copy state_dict so _load_from_state_dict can modify it
        _lowerCAmelCase		     = getattr(SCREAMING_SNAKE_CASE_						,    "_metadata"						,    SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = state_dict.copy()
        if metadata is not None:
            _lowerCAmelCase		     = metadata

        def load(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   List[str]=""      ):
            _lowerCAmelCase		     = {} if metadata is None else metadata.get(prefix[:-1]						,    {}      )
            module._load_from_state_dict(
                SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )
            for name, child in module._modules.items():
                if child is not None:
                    load(SCREAMING_SNAKE_CASE_						,    prefix + name + "."      )

        load(SCREAMING_SNAKE_CASE_						,    prefix=SCREAMING_SNAKE_CASE_      )

        _lowerCAmelCase		     = []
        _lowerCAmelCase		     = []
        for key in missing_keys:
            _lowerCAmelCase		     = True
            for ignore_key in ignore_missing.split("|"      ):
                if ignore_key in key:
                    _lowerCAmelCase		     = False
                    break
            if keep_flag:
                warn_missing_keys.append(SCREAMING_SNAKE_CASE_      )
            else:
                ignore_missing_keys.append(SCREAMING_SNAKE_CASE_      )

        _lowerCAmelCase		     = warn_missing_keys

        if len(SCREAMING_SNAKE_CASE_      ) > 0:
            print(
                "Weights of {} not initialized from pretrained model: {}".format(
                    model.__class__.__name__						,    SCREAMING_SNAKE_CASE_      )      )
        if len(SCREAMING_SNAKE_CASE_      ) > 0:
            print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__						,    SCREAMING_SNAKE_CASE_      )      )
        if len(SCREAMING_SNAKE_CASE_      ) > 0:
            print(
                "Ignored weights of {} not initialized from pretrained model: {}".format(
                    model.__class__.__name__						,    SCREAMING_SNAKE_CASE_      )      )
        if len(SCREAMING_SNAKE_CASE_      ) > 0:
            print("\n".join(SCREAMING_SNAKE_CASE_      )      )

    _lowerCAmelCase		     = {
        'pretrained': False,
        'use_shared_rel_pos_bias': True,
        'use_abs_pos_emb': False,
        'init_values': 0.1,
    }

    if is_finetuned:
        model_kwargs.update(
            {
                "num_classes": 1000,
                "use_mean_pooling": True,
                "init_scale": 0.001,
                "use_rel_pos_bias": True,
            }      )

    _lowerCAmelCase		     = create_model(
        "beit_large_patch16_224" if is_large else "beit_base_patch16_224"						,    **SCREAMING_SNAKE_CASE_						,    )
    _lowerCAmelCase		     = model.patch_embed.patch_size
    _lowerCAmelCase		     = (args.input_size // patch_size[0], args.input_size // patch_size[1])
    _lowerCAmelCase		     = torch.load(args.beit_checkpoint						,    map_location="cpu"      )

    print(F'''Load ckpt from {args.beit_checkpoint}'''      )
    _lowerCAmelCase		     = None
    for model_key in ("model", "module"):
        if model_key in checkpoint:
            _lowerCAmelCase		     = checkpoint[model_key]
            print(F'''Load state_dict by model_key = {model_key}'''      )
            break

    _lowerCAmelCase		     = list(checkpoint_model.keys()      )
    for key in all_keys:
        if "relative_position_index" in key:
            checkpoint_model.pop(SCREAMING_SNAKE_CASE_      )

        if "relative_position_bias_table" in key:
            _lowerCAmelCase		     = checkpoint_model[key]
            _lowerCAmelCase		     = rel_pos_bias.size()
            _lowerCAmelCase		     = model.state_dict()[key].size()
            _lowerCAmelCase		     = model.patch_embed.patch_shape
            if dst_patch_shape[0] != dst_patch_shape[1]:
                raise NotImplementedError()

    load_state_dict(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    prefix=""      )

    return model


def 		__a():



    '''simple docstring'''





    _lowerCAmelCase		     = get_args()

    _lowerCAmelCase		     = 'ft1k' in args.hf_checkpoint_name
    _lowerCAmelCase		     = 'large' in args.hf_checkpoint_name

    if is_finetuned:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
        # into this folder.
        import modeling_finetune  # noqa: F401
    else:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
        # into this folder
        # IMPORTANT: Note that for now we've only converted the down-stream
        # model and not the full pretrained model. This means for the integration
        # test you need to add a `return x` after the following line:
        # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
        # to make the integration test pass.
        import modeling_cyclical  # noqa: F401

    # 1. Create model config
    _lowerCAmelCase		     = DataaVecVisionConfig()
    if is_finetuned:
        _lowerCAmelCase		     = True
        _lowerCAmelCase		     = False
        _lowerCAmelCase		     = True
        _lowerCAmelCase		     = 1000

        _lowerCAmelCase		     = 'huggingface/label-files'
        _lowerCAmelCase		     = 'imagenet-1k-id2label.json'
        _lowerCAmelCase		     = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    repo_type="dataset"      )						,    "r"      )      )
        _lowerCAmelCase		     = {int(SCREAMING_SNAKE_CASE_      ): v for k, v in idalabel.items()}
        _lowerCAmelCase		     = idalabel
        _lowerCAmelCase		     = {v: k for k, v in idalabel.items()}
    else:
        _lowerCAmelCase		     = False
        _lowerCAmelCase		     = True
        _lowerCAmelCase		     = False

    if is_large:
        _lowerCAmelCase		     = 1024
        _lowerCAmelCase		     = 4096
        _lowerCAmelCase		     = 24
        _lowerCAmelCase		     = 16

    # 2. Load Beit model
    _lowerCAmelCase		     = load_beit_model(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )
    orig_model.eval()

    # 3. Forward Beit model
    _lowerCAmelCase		     = BeitImageProcessor(size=config.image_size						,    do_center_crop=SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"      )
    _lowerCAmelCase		     = image_processor(images=SCREAMING_SNAKE_CASE_						,    return_tensors="pt"      )
    _lowerCAmelCase		     = encoding['pixel_values']

    _lowerCAmelCase		     = (pixel_values,) if is_finetuned else (pixel_values, None)
    with torch.no_grad():
        _lowerCAmelCase		     = orig_model(*SCREAMING_SNAKE_CASE_      )

    # 4. Load HF Data2VecVision model
    if is_finetuned:
        _lowerCAmelCase		     = DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_      )
        hf_model.eval()
        _lowerCAmelCase		     = False
        _lowerCAmelCase		     = 'data2vec_vision.'
    else:
        _lowerCAmelCase		     = DataaVecVisionModel(SCREAMING_SNAKE_CASE_      )
        hf_model.eval()
        _lowerCAmelCase		     = True
        _lowerCAmelCase		     = ''

    _lowerCAmelCase		     = create_rename_keys(SCREAMING_SNAKE_CASE_						,    hf_prefix=SCREAMING_SNAKE_CASE_						,    has_lm_head=SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = orig_model.state_dict()
    for src, dest in rename_keys:
        _lowerCAmelCase		     = state_dict.pop(SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = val

    read_in_q_k_v(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    hf_prefix=SCREAMING_SNAKE_CASE_						,    has_lm_head=SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = hf_model.load_state_dict(SCREAMING_SNAKE_CASE_						,    strict=SCREAMING_SNAKE_CASE_      )
    print("HF missing"						,    SCREAMING_SNAKE_CASE_      )
    print("HF unexpected_keys"						,    SCREAMING_SNAKE_CASE_      )

    # 5. Forward HF Data2VecVision model
    with torch.no_grad():
        _lowerCAmelCase		     = hf_model(SCREAMING_SNAKE_CASE_      )

    _lowerCAmelCase		     = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

    # 6. Compare
    _lowerCAmelCase		     = torch.max(torch.abs(hf_output - orig_model_output      )      ).item()

    print(F'''max_absolute_diff = {max_absolute_diff}'''      )
    _lowerCAmelCase		     = torch.allclose(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    atol=1e-3      )
    print("Do both models output the same tensors?"						,    "🔥" if success else "💩"      )
    if not success:
        raise Exception("Something went wRoNg"      )

    # 7. Save
    print(F'''Saving to {args.hf_checkpoint_name}'''      )
    hf_model.save_pretrained(args.hf_checkpoint_name      )
    image_processor.save_pretrained(args.hf_checkpoint_name      )


if __name__ == "__main__":
       main()
       # Run the following to convert checkpoints
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"









'''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
       _SCREAMING_SNAKE_CASE			=							"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(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   Optional[int]=None						,    SCREAMING_SNAKE_CASE_					:   int=None						,    SCREAMING_SNAKE_CASE_					:   int=None						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]=None						,    SCREAMING_SNAKE_CASE_					:   Any=None						,    SCREAMING_SNAKE_CASE_					:   Tuple=None						,    ):



    '''simple docstring'''





    if attention_mask is None:
        _lowerCAmelCase		     = np.where(input_ids != config.pad_token_id						,    1						,    0      )
    if decoder_attention_mask is None:
        _lowerCAmelCase		     = np.where(decoder_input_ids != config.pad_token_id						,    1						,    0      )
    if head_mask is None:
        _lowerCAmelCase		     = np.ones((config.encoder_layers, config.encoder_attention_heads)      )
    if decoder_head_mask is None:
        _lowerCAmelCase		     = np.ones((config.decoder_layers, config.decoder_attention_heads)      )
    if cross_attn_head_mask is None:
        _lowerCAmelCase		     = 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 				lowerCAmelCase_						(						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=0.02     ,       )      ->							List[Any]:
     _lowerCAmelCase		     = parent
     _lowerCAmelCase		     = batch_size
     _lowerCAmelCase		     = seq_length
     _lowerCAmelCase		     = is_training
     _lowerCAmelCase		     = use_labels
     _lowerCAmelCase		     = vocab_size
     _lowerCAmelCase		     = hidden_size
     _lowerCAmelCase		     = num_hidden_layers
     _lowerCAmelCase		     = num_attention_heads
     _lowerCAmelCase		     = intermediate_size
     _lowerCAmelCase		     = hidden_act
     _lowerCAmelCase		     = hidden_dropout_prob
     _lowerCAmelCase		     = attention_probs_dropout_prob
     _lowerCAmelCase		     = max_position_embeddings
     _lowerCAmelCase		     = eos_token_id
     _lowerCAmelCase		     = pad_token_id
     _lowerCAmelCase		     = bos_token_id
     _lowerCAmelCase		     = decoder_start_token_id
     _lowerCAmelCase		     = initializer_range




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

     _lowerCAmelCase		     = shift_tokens_right(_lowerCAmelCase     ,       1						)

     _lowerCAmelCase		     = 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     ,       )
     _lowerCAmelCase		     = prepare_mbart_inputs_dict(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)
     return config, inputs_dict




 def 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.prepare_config_and_inputs()
     return config, inputs_dict




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Optional[Any]:
     _lowerCAmelCase		     = 20
     _lowerCAmelCase		     = model_class_name(_lowerCAmelCase						)

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

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

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

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

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

     _lowerCAmelCase		     = model.decode(_lowerCAmelCase     ,       _lowerCAmelCase						)

     _lowerCAmelCase		     = 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						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = 20
     _lowerCAmelCase		     = model_class_name(_lowerCAmelCase						)

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

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

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

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

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

     _lowerCAmelCase		     = model.decode(_lowerCAmelCase     ,       _lowerCAmelCase     ,       decoder_attention_mask=_lowerCAmelCase						)

     _lowerCAmelCase		     = 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 				lowerCAmelCase_						(						unittest.TestCase						):
 __lowerCamelCase  :							Optional[int]    					=   99




 def 							_snake_case				(		self						)      ->							Tuple:
     _lowerCAmelCase		     = 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     ,       )

     _lowerCAmelCase		     = input_ids.shape[0]
     _lowerCAmelCase		     = 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						)      ->							Any:
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = self._get_config_and_data()
     _lowerCAmelCase		     = FlaxMBartForSequenceClassification(_lowerCAmelCase						)
     _lowerCAmelCase		     = model(input_ids=_lowerCAmelCase     ,       decoder_input_ids=_lowerCAmelCase						)
     _lowerCAmelCase		     = (batch_size, config.num_labels)
     self.assertEqual(outputs["logits"].shape     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = self._get_config_and_data()
     _lowerCAmelCase		     = FlaxMBartForQuestionAnswering(_lowerCAmelCase						)
     _lowerCAmelCase		     = 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						)      ->							List[Any]:
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = self._get_config_and_data()
     _lowerCAmelCase		     = FlaxMBartForConditionalGeneration(_lowerCAmelCase						)
     _lowerCAmelCase		     = lm_model(input_ids=_lowerCAmelCase						)
     _lowerCAmelCase		     = (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs["logits"].shape     ,       _lowerCAmelCase						)




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





 def 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase		     = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,       dtype=np.intaa						)
     _lowerCAmelCase		     = shift_tokens_right(_lowerCAmelCase     ,       1						)
     _lowerCAmelCase		     = np.equal(_lowerCAmelCase     ,       1						).astype(np.floataa						).sum()
     _lowerCAmelCase		     = 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 				lowerCAmelCase_						(						__magic_name__  ,unittest.TestCase  ,__magic_name__						):
 __lowerCamelCase  :							List[Any]    					=   True
 __lowerCamelCase  :							Tuple    					=   (
     (
         FlaxMBartModel,
         FlaxMBartForConditionalGeneration,
         FlaxMBartForSequenceClassification,
         FlaxMBartForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 __lowerCamelCase  :							str    					=   (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




 def 							_snake_case				(		self						)      ->							Tuple:
     _lowerCAmelCase		     = FlaxMBartModelTester(self						)




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = 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						)      ->							str:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = 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						)      ->							Optional[int]:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs_for_common()

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

             with self.subTest("JIT Disabled"						):
                 with jax.disable_jit():
                     _lowerCAmelCase		     = 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						)      ->							str:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs_for_common()

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

             _lowerCAmelCase		     = {
                 "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"						):
                 _lowerCAmelCase		     = decode_jitted(**_lowerCAmelCase						).to_tuple()

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







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




 @cached_property
 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     return AutoTokenizer.from_pretrained(self.model_name						)




 @cached_property
 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name     ,       from_pt=_lowerCAmelCase						)
     return model




 def 							_snake_case				(		self     ,       **_lowerCAmelCase						)      ->							int:
     _lowerCAmelCase		     = self.translate_src_text(**_lowerCAmelCase						)
     self.assertListEqual(self.expected_text     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self     ,       **_lowerCAmelCase						)      ->							Optional[Any]:
     _lowerCAmelCase		     = self.tokenizer(self.src_text     ,       **_lowerCAmelCase     ,       return_tensors="np"						)
     _lowerCAmelCase		     = 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
     _lowerCAmelCase		     = self.tokenizer.batch_decode(_lowerCAmelCase     ,       skip_special_tokens=_lowerCAmelCase						)
     return generated_words





 @slow
 def 							_snake_case				(		self						)      ->							Optional[Any]:
     self._assert_generated_batch_equal_expected()


