





"""simple docstring"""





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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Dict							,						SCREAMING_SNAKE_CASE					:						str=False							,						SCREAMING_SNAKE_CASE					:						str=False							,						SCREAMING_SNAKE_CASE					:						Optional[Any]="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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						int=False							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]=False							,						SCREAMING_SNAKE_CASE					:						int="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       UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=	argparse.ArgumentParser(
          """Convert Data2VecVision to HF for image classification and pretraining"""							,						add_help=snake_case__      )
      parser.add_argument("""--hf_checkpoint_name"""							,						type=snake_case__      )
      parser.add_argument("""--input_size"""							,						default=2_24							,						type=snake_case__							,						help="""images input size"""      )
      parser.add_argument("""--beit_checkpoint"""							,						default=""""""							,						help="""beit checkpoint"""      )

      return parser.parse_args()


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Dict							,						SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						Optional[Any]      ):







      '''simple docstring'''




      def load_state_dict(SCREAMING_SNAKE_CASE					:						List[Any]							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]							,						SCREAMING_SNAKE_CASE					:						Tuple=""							,						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(snake_case__							,						"""_metadata"""							,						snake_case__      )
            lowerCAmelCase  						=	state_dict.copy()
            if metadata is not None:
                  lowerCAmelCase  						=	metadata

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

            load(snake_case__							,						prefix=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(snake_case__      )
                  else:
                        ignore_missing_keys.append(snake_case__      )

            lowerCAmelCase  						=	warn_missing_keys

            if len(snake_case__      ) > 0:
                  print(
                      """Weights of {} not initialized from pretrained model: {}""".format(
                          model.__class__.__name__							,						snake_case__      )      )
            if len(snake_case__      ) > 0:
                  print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__							,						snake_case__      )      )
            if len(snake_case__      ) > 0:
                  print(
                      """Ignored weights of {} not initialized from pretrained model: {}""".format(
                          model.__class__.__name__							,						snake_case__      )      )
            if len(snake_case__      ) > 0:
                  print("""\n""".join(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""": 10_00,
                    """use_mean_pooling""": True,
                    """init_scale""": 0.0_01,
                    """use_rel_pos_bias""": True,
                }      )

      lowerCAmelCase  						=	create_model(
          """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""							,						**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(snake_case__      )

            if "relative_position_bias_table" in key:
                  lowerCAmelCase  						=	checkpoint_model[key]
                  lowerCAmelCase						,		lowerCAmelCase  						=	rel_pos_bias.size()
                  lowerCAmelCase						,		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(snake_case__							,						snake_case__							,						prefix=""""""      )

      return model


def       UpperCAmelCase__							(  ):







      '''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  						=	10_00

            lowerCAmelCase  						=	"""huggingface/label-files"""
            lowerCAmelCase  						=	"""imagenet-1k-id2label.json"""
            lowerCAmelCase  						=	json.load(open(hf_hub_download(snake_case__							,						snake_case__							,						repo_type="""dataset"""      )							,						"""r"""      )      )
            lowerCAmelCase  						=	{int(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  						=	10_24
            lowerCAmelCase  						=	40_96
            lowerCAmelCase  						=	24
            lowerCAmelCase  						=	16

      # 2. Load Beit model
      lowerCAmelCase  						=	load_beit_model(snake_case__							,						snake_case__							,						snake_case__      )
      orig_model.eval()

      # 3. Forward Beit model
      lowerCAmelCase  						=	BeitImageProcessor(size=config.image_size							,						do_center_crop=snake_case__      )
      lowerCAmelCase  						=	Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""      )
      lowerCAmelCase  						=	image_processor(images=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(*snake_case__      )

      # 4. Load HF Data2VecVision model
      if is_finetuned:
            lowerCAmelCase  						=	DataaVecVisionForImageClassification(snake_case__      )
            hf_model.eval()
            lowerCAmelCase  						=	False
            lowerCAmelCase  						=	"""data2vec_vision."""
      else:
            lowerCAmelCase  						=	DataaVecVisionModel(snake_case__      )
            hf_model.eval()
            lowerCAmelCase  						=	True
            lowerCAmelCase  						=	""""""

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

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

      # 5. Forward HF Data2VecVision model
      with torch.no_grad():
            lowerCAmelCase  						=	hf_model(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(snake_case__							,						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"""





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 		lowercase :
      _SCREAMING_SNAKE_CASE         =				MarianConfig
      _SCREAMING_SNAKE_CASE         =				{}
      _SCREAMING_SNAKE_CASE         =				'gelu'





      def __init__(	self				,						lowercase				,						lowercase=13				,						lowercase=7				,						lowercase=True				,						lowercase=False				,						lowercase=99				,						lowercase=32				,						lowercase=2				,						lowercase=4				,						lowercase=37				,						lowercase=0.1				,						lowercase=0.1				,						lowercase=20				,						lowercase=2				,						lowercase=1				,						lowercase=0				,						)		->					int:
            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_dropout_prob
            lowerCAmelCase  						=	attention_probs_dropout_prob
            lowerCAmelCase  						=	max_position_embeddings
            lowerCAmelCase  						=	eos_token_id
            lowerCAmelCase  						=	pad_token_id
            lowerCAmelCase  						=	bos_token_id





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	ids_tensor([self.batch_size, self.seq_length - 1]				,						self.vocab_size			)
            lowerCAmelCase  						=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)				,						1			)
            lowerCAmelCase  						=	tf.concat([input_ids, eos_tensor]				,						axis=1			)

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

            lowerCAmelCase  						=	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				,						)
            lowerCAmelCase  						=	prepare_marian_inputs_dict(lowercase				,						lowercase				,						lowercase			)
            return config, inputs_dict





      def    _snake_case      (	self				,						lowercase				,						lowercase			)		->					Union[str, Any]:
            lowerCAmelCase  						=	TFMarianModel(config=lowercase			).get_decoder()
            lowerCAmelCase  						=	inputs_dict["""input_ids"""]

            lowerCAmelCase  						=	input_ids[:1, :]
            lowerCAmelCase  						=	inputs_dict["""attention_mask"""][:1, :]
            lowerCAmelCase  						=	inputs_dict["""head_mask"""]
            lowerCAmelCase  						=	1

            # first forward pass
            lowerCAmelCase  						=	model(lowercase				,						attention_mask=lowercase				,						head_mask=lowercase				,						use_cache=lowercase			)

            lowerCAmelCase						,		lowerCAmelCase  						=	outputs.to_tuple()

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

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

            lowerCAmelCase  						=	model(lowercase				,						attention_mask=lowercase			)[0]
            lowerCAmelCase  						=	model(lowercase				,						attention_mask=lowercase				,						past_key_values=lowercase			)[0]

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

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

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







def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						List[str]							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]							,						SCREAMING_SNAKE_CASE					:						Any=None							,						SCREAMING_SNAKE_CASE					:						str=None							,						SCREAMING_SNAKE_CASE					:						List[str]=None							,						SCREAMING_SNAKE_CASE					:						Tuple=None							,						SCREAMING_SNAKE_CASE					:						Dict=None							,						):







      '''simple docstring'''




      if attention_mask is None:
            lowerCAmelCase  						=	tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE							,						config.pad_token_id      )							,						tf.inta      )
      if decoder_attention_mask is None:
            lowerCAmelCase  						=	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:
            lowerCAmelCase  						=	tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
      if decoder_head_mask is None:
            lowerCAmelCase  						=	tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
      if cross_attn_head_mask is None:
            lowerCAmelCase  						=	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 		lowercase ( _UpperCAmelCase					,  _UpperCAmelCase					,  unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE         =				(TFMarianMTModel,) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE         =				(
          {
              'conversational': TFMarianMTModel,
              'feature-extraction': TFMarianModel,
              'summarization': TFMarianMTModel,
              'text2text-generation': TFMarianMTModel,
              'translation': TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE         =				True
      _SCREAMING_SNAKE_CASE         =				False
      _SCREAMING_SNAKE_CASE         =				False





      def    _snake_case      (	self			)		->					Optional[int]:
            lowerCAmelCase  						=	TFMarianModelTester(self			)
            lowerCAmelCase  						=	ConfigTester(self				,						config_class=lowercase			)





      def    _snake_case      (	self			)		->					str:
            self.config_tester.run_common_tests()





      def    _snake_case      (	self			)		->					str:
            lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*lowercase			)







@require_tf
class 		lowercase ( unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				1_000  # show more chars for failing integration tests





      @classmethod
      def    _snake_case      (	cls			)		->					None:
            lowerCAmelCase  						=	f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
            return cls





      @cached_property
      def    _snake_case      (	self			)		->					MarianTokenizer:
            return AutoTokenizer.from_pretrained(self.model_name			)





      @property
      def    _snake_case      (	self			)		->					int:
            return self.tokenizer.eos_token_id





      @cached_property
      def    _snake_case      (	self			)		->					Any:
            warnings.simplefilter("""error"""			)
            lowerCAmelCase  						=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
            assert isinstance(lowercase				,						lowercase			)
            lowerCAmelCase  						=	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    _snake_case      (	self				,						**lowercase			)		->					Tuple:
            lowerCAmelCase  						=	self.translate_src_text(**lowercase			)
            self.assertListEqual(self.expected_text				,						lowercase			)





      def    _snake_case      (	self				,						**lowercase			)		->					Union[str, Any]:
            lowerCAmelCase  						=	self.tokenizer(self.src_text				,						**lowercase				,						padding=lowercase				,						return_tensors="""tf"""			)
            lowerCAmelCase  						=	self.model.generate(
                model_inputs.input_ids				,						attention_mask=model_inputs.attention_mask				,						num_beams=2				,						max_length=128			)
            lowerCAmelCase  						=	self.tokenizer.batch_decode(generated_ids.numpy()				,						skip_special_tokens=lowercase			)
            return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'mt'
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      _SCREAMING_SNAKE_CASE         =				['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']





      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def    _snake_case      (	self			)		->					List[Any]:
            self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				'zh'
      _SCREAMING_SNAKE_CASE         =				['My name is Wolfgang and I live in Berlin']
      _SCREAMING_SNAKE_CASE         =				['我叫沃尔夫冈 我住在柏林']





      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def    _snake_case      (	self			)		->					Dict:
            self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				'ROMANCE'
      _SCREAMING_SNAKE_CASE         =				[
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      _SCREAMING_SNAKE_CASE         =				[
          '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    _snake_case      (	self			)		->					List[str]:
            self._assert_generated_batch_equal_expected()





      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	TranslationPipeline(self.model				,						self.tokenizer				,						framework="""tf"""			)
            lowerCAmelCase  						=	pipeline(self.src_text			)
            self.assertEqual(self.expected_text				,						[x["""translation_text"""] for x in output]			)






