





"""simple docstring"""


import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__				  =   logging.get_logger(__name__)




def 					lowercase__    (					lowercase_			)					->		Optional[int]:




      """simple docstring"""

      print("Loading config file..."			)

      def flatten_yaml_as_dict(lowercase_		,lowercase_=""		,lowercase_="."			):
            _UpperCamelCase      :	int						=       []
            for k, v in d.items():
                  _UpperCamelCase      :	Optional[int]						=       parent_key + sep + k if parent_key else k
                  if isinstance(lowercase_		,collections.abc.MutableMapping			):
                        items.extend(flatten_yaml_as_dict(lowercase_		,lowercase_		,sep=lowercase_			).items()			)
                  else:
                        items.append((new_key, v)			)
            return dict(lowercase_			)

      _UpperCamelCase      :	Optional[int]						=       argparse.Namespace()
      with open(lowercase_		,"r"			) as yaml_file:
            try:
                  _UpperCamelCase      :	str						=       yaml.load(lowercase_		,Loader=yaml.FullLoader			)

                  _UpperCamelCase      :	Union[str, Any]						=       flatten_yaml_as_dict(lowercase_			)
                  for k, v in flat_cfg.items():
                        setattr(lowercase_		,lowercase_		,lowercase_			)
            except yaml.YAMLError as exc:
                  logger.error("Error while loading config file: {}. Error message: {}".format(lowercase_		,str(lowercase_			)			)			)
      return config




def 					lowercase__    (					lowercase_		,lowercase_			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       MobileViTVaConfig()

      _UpperCamelCase      :	List[str]						=       False

      # dataset
      if task_name.startswith("imagenet1k_"			):
            _UpperCamelCase      :	List[str]						=       1_000
            if int(task_name.strip().split("_"			)[-1]			) == 384:
                  _UpperCamelCase      :	Optional[Any]						=       384
            else:
                  _UpperCamelCase      :	Dict						=       256
            _UpperCamelCase      :	List[Any]						=       "imagenet-1k-id2label.json"
      elif task_name.startswith("imagenet21k_to_1k_"			):
            _UpperCamelCase      :	Tuple						=       21_000
            if int(task_name.strip().split("_"			)[-1]			) == 384:
                  _UpperCamelCase      :	int						=       384
            else:
                  _UpperCamelCase      :	List[Any]						=       256
            _UpperCamelCase      :	Any						=       "imagenet-22k-id2label.json"
      elif task_name.startswith("ade20k_"			):
            _UpperCamelCase      :	Union[str, Any]						=       151
            _UpperCamelCase      :	List[str]						=       512
            _UpperCamelCase      :	int						=       "ade20k-id2label.json"
            _UpperCamelCase      :	Optional[Any]						=       True
      elif task_name.startswith("voc_"			):
            _UpperCamelCase      :	Any						=       21
            _UpperCamelCase      :	Any						=       512
            _UpperCamelCase      :	Dict						=       "pascal-voc-id2label.json"
            _UpperCamelCase      :	Union[str, Any]						=       True

      # orig_config
      _UpperCamelCase      :	Dict						=       load_orig_config_file(lowercase_			)
      assert getattr(lowercase_		,"model.classification.name"		,-1			) == "mobilevit_v2", "Invalid model"
      _UpperCamelCase      :	Optional[int]						=       getattr(lowercase_		,"model.classification.mitv2.width_multiplier"		,1.0			)
      assert (
          getattr(lowercase_		,"model.classification.mitv2.attn_norm_layer"		,-1			) == "layer_norm_2d"
      ), "Norm layers other than layer_norm_2d is not supported"
      _UpperCamelCase      :	List[str]						=       getattr(lowercase_		,"model.classification.activation.name"		,"swish"			)
      # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

      if is_segmentation_model:
            _UpperCamelCase      :	Optional[int]						=       getattr(lowercase_		,"model.segmentation.output_stride"		,16			)
            if "_deeplabv3" in task_name:
                  _UpperCamelCase      :	Tuple						=       getattr(lowercase_		,"model.segmentation.deeplabv3.aspp_rates"		,[12, 24, 36]			)
                  _UpperCamelCase      :	Any						=       getattr(lowercase_		,"model.segmentation.deeplabv3.aspp_out_channels"		,512			)
                  _UpperCamelCase      :	Dict						=       getattr(lowercase_		,"model.segmentation.deeplabv3.aspp_dropout"		,0.1			)

    # id2label
      _UpperCamelCase      :	List[str]						=       "huggingface/label-files"
      _UpperCamelCase      :	Dict						=       json.load(open(hf_hub_download(lowercase_		,lowercase_		,repo_type="dataset"			)		,"r"			)			)
      _UpperCamelCase      :	Any						=       {int(lowercase_			): v for k, v in idalabel.items()}
      _UpperCamelCase      :	Any						=       idalabel
      _UpperCamelCase      :	Optional[int]						=       {v: k for k, v in idalabel.items()}

      return config




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	Any						=       dct.pop(lowercase_			)
      _UpperCamelCase      :	Union[str, Any]						=       val




def 					lowercase__    (					lowercase_		,lowercase_=False			)					->		Optional[int]:




      """simple docstring"""

      if base_model:
            _UpperCamelCase      :	Dict						=       ""
      else:
            _UpperCamelCase      :	str						=       "mobilevitv2."

      _UpperCamelCase      :	Any						=       []
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  _UpperCamelCase      :	str						=       k[8:]
            else:
                  _UpperCamelCase      :	Dict						=       k

            if ".block." in k:
                  _UpperCamelCase      :	Union[str, Any]						=       k_new.replace(".block."		,"."			)
            if ".conv." in k:
                  _UpperCamelCase      :	Tuple						=       k_new.replace(".conv."		,".convolution."			)
            if ".norm." in k:
                  _UpperCamelCase      :	Tuple						=       k_new.replace(".norm."		,".normalization."			)

            if "conv_1." in k:
                  _UpperCamelCase      :	Optional[Any]						=       k_new.replace("conv_1."		,F'''{model_prefix}conv_stem.'''			)
            for i in [1, 2]:
                  if F'''layer_{i}.''' in k:
                        _UpperCamelCase      :	Any						=       k_new.replace(F'''layer_{i}.'''		,F'''{model_prefix}encoder.layer.{i-1}.layer.'''			)
            if ".exp_1x1." in k:
                  _UpperCamelCase      :	Tuple						=       k_new.replace(".exp_1x1."		,".expand_1x1."			)
            if ".red_1x1." in k:
                  _UpperCamelCase      :	Optional[int]						=       k_new.replace(".red_1x1."		,".reduce_1x1."			)

            for i in [3, 4, 5]:
                  if F'''layer_{i}.0.''' in k:
                        _UpperCamelCase      :	Any						=       k_new.replace(F'''layer_{i}.0.'''		,F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''			)
                  if F'''layer_{i}.1.local_rep.0.''' in k:
                        _UpperCamelCase      :	List[str]						=       k_new.replace(F'''layer_{i}.1.local_rep.0.'''		,F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''			)
                  if F'''layer_{i}.1.local_rep.1.''' in k:
                        _UpperCamelCase      :	int						=       k_new.replace(F'''layer_{i}.1.local_rep.1.'''		,F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''			)

            for i in [3, 4, 5]:
                  if i == 3:
                        _UpperCamelCase      :	Union[str, Any]						=       [0, 1]
                  elif i == 4:
                        _UpperCamelCase      :	Tuple						=       [0, 1, 2, 3]
                  elif i == 5:
                        _UpperCamelCase      :	List[str]						=       [0, 1, 2]

                  for j in j_in:
                        if F'''layer_{i}.1.global_rep.{j}.''' in k:
                              _UpperCamelCase      :	str						=       k_new.replace(
                                  F'''layer_{i}.1.global_rep.{j}.'''		,F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''			)
                  if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
                        _UpperCamelCase      :	Dict						=       k_new.replace(
                            F'''layer_{i}.1.global_rep.{j+1}.'''		,F'''{model_prefix}encoder.layer.{i-1}.layernorm.'''			)

                  if F'''layer_{i}.1.conv_proj.''' in k:
                        _UpperCamelCase      :	Optional[Any]						=       k_new.replace(F'''layer_{i}.1.conv_proj.'''		,F'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''			)

            if "pre_norm_attn.0." in k:
                  _UpperCamelCase      :	Dict						=       k_new.replace("pre_norm_attn.0."		,"layernorm_before."			)
            if "pre_norm_attn.1." in k:
                  _UpperCamelCase      :	List[str]						=       k_new.replace("pre_norm_attn.1."		,"attention."			)
            if "pre_norm_ffn.0." in k:
                  _UpperCamelCase      :	Optional[int]						=       k_new.replace("pre_norm_ffn.0."		,"layernorm_after."			)
            if "pre_norm_ffn.1." in k:
                  _UpperCamelCase      :	int						=       k_new.replace("pre_norm_ffn.1."		,"ffn.conv1."			)
            if "pre_norm_ffn.3." in k:
                  _UpperCamelCase      :	Any						=       k_new.replace("pre_norm_ffn.3."		,"ffn.conv2."			)

            if "classifier.1." in k:
                  _UpperCamelCase      :	int						=       k_new.replace("classifier.1."		,"classifier."			)

            if "seg_head." in k:
                  _UpperCamelCase      :	Tuple						=       k_new.replace("seg_head."		,"segmentation_head."			)
            if ".aspp_layer." in k:
                  _UpperCamelCase      :	List[Any]						=       k_new.replace(".aspp_layer."		,"."			)
            if ".aspp_pool." in k:
                  _UpperCamelCase      :	Tuple						=       k_new.replace(".aspp_pool."		,"."			)

            rename_keys.append((k, k_new)			)
      return rename_keys




def 					lowercase__    (					lowercase_			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	Dict						=       []
      for k in state_dict.keys():
            if k.startswith("seg_head.aux_head."			):
                  keys_to_ignore.append(lowercase_			)
      for k in keys_to_ignore:
            state_dict.pop(lowercase_		,lowercase_			)




def 					lowercase__    (					)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       "http://images.cocodataset.org/val2017/000000039769.jpg"
      # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
      _UpperCamelCase      :	Union[str, Any]						=       Image.open(requests.get(lowercase_		,stream=lowercase_			).raw			)
      return im




@torch.no_grad()
def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       get_mobilevitva_config(lowercase_		,lowercase_			)

      # load original state_dict
      _UpperCamelCase      :	str						=       torch.load(lowercase_		,map_location="cpu"			)

      # load huggingface model
      if task_name.startswith("ade20k_"			) or task_name.startswith("voc_"			):
            _UpperCamelCase      :	Optional[int]						=       MobileViTVaForSemanticSegmentation(lowercase_			).eval()
            _UpperCamelCase      :	Any						=       False
      else:
            _UpperCamelCase      :	List[Any]						=       MobileViTVaForImageClassification(lowercase_			).eval()
            _UpperCamelCase      :	List[str]						=       False

      # remove and rename some keys of load the original model
      _UpperCamelCase      :	Dict						=       checkpoint
      remove_unused_keys(lowercase_			)
      _UpperCamelCase      :	Optional[Any]						=       create_rename_keys(lowercase_		,base_model=lowercase_			)
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(lowercase_		,lowercase_		,lowercase_			)

      # load modified state_dict
      model.load_state_dict(lowercase_			)

      # Check outputs on an image, prepared by MobileViTImageProcessor
      _UpperCamelCase      :	Tuple						=       MobileViTImageProcessor(crop_size=config.image_size		,size=config.image_size + 32			)
      _UpperCamelCase      :	Dict						=       image_processor(images=prepare_img()		,return_tensors="pt"			)
      _UpperCamelCase      :	int						=       model(**lowercase_			)

      # verify classification model
      if task_name.startswith("imagenet"			):
            _UpperCamelCase      :	Tuple						=       outputs.logits
            _UpperCamelCase      :	Tuple						=       logits.argmax(-1			).item()
            print("Predicted class:"		,model.config.idalabel[predicted_class_idx]			)
            if task_name.startswith("imagenet1k_256"			) and config.width_multiplier == 1.0:
                  # expected_logits for base variant
                  _UpperCamelCase      :	Optional[int]						=       torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]			)
                  assert torch.allclose(logits[0, :3]		,lowercase_		,atol=1e-4			)

      Path(lowercase_			).mkdir(exist_ok=lowercase_			)
      print(F'''Saving model {task_name} to {pytorch_dump_folder_path}'''			)
      model.save_pretrained(lowercase_			)
      print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
      image_processor.save_pretrained(lowercase_			)


if __name__ == "__main__":
  lowerCamelCase__				  =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--task",
      default="imagenet1k_256",
      type=str,
      help=(
          "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
          "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
      ),
      choices=[
          "imagenet1k_256",
          "imagenet1k_384",
          "imagenet21k_to_1k_256",
          "imagenet21k_to_1k_384",
          "ade20k_deeplabv3",
          "voc_deeplabv3",
      ],
  )

  parser.add_argument(
      "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
  )
  parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
  )

  lowerCamelCase__				  =   parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )







"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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 TFLEDForConditionalGeneration, TFLEDModel



@require_tf
class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any	     =					LEDConfig
  SCREAMING_SNAKE_CASE__							:str	     =					{}
  SCREAMING_SNAKE_CASE__							:List[str]	     =					"gelu"

  def __init__(		self   :       List[Any]					,   __a   :       Union[str, Any]					,   __a   :       List[Any]=13					,   __a   :       int=7					,   __a   :       str=True					,   __a   :       Any=False					,   __a   :       str=99					,   __a   :       str=32					,   __a   :       Union[str, Any]=2					,   __a   :       Optional[Any]=4					,   __a   :       List[Any]=37					,   __a   :       List[Any]=0.1					,   __a   :       Tuple=0.1					,   __a   :       Dict=20					,   __a   :       str=2					,   __a   :       Dict=1					,   __a   :       Any=0					,   __a   :       List[Any]=4					,   )		->				List[Any]:
        _UpperCamelCase      :	Optional[Any]						=       parent
        _UpperCamelCase      :	List[str]						=       batch_size
        _UpperCamelCase      :	str						=       seq_length
        _UpperCamelCase      :	str						=       is_training
        _UpperCamelCase      :	Any						=       use_labels
        _UpperCamelCase      :	Any						=       vocab_size
        _UpperCamelCase      :	List[str]						=       hidden_size
        _UpperCamelCase      :	Optional[Any]						=       num_hidden_layers
        _UpperCamelCase      :	Dict						=       num_attention_heads
        _UpperCamelCase      :	Optional[Any]						=       intermediate_size

        _UpperCamelCase      :	int						=       hidden_dropout_prob
        _UpperCamelCase      :	Dict						=       attention_probs_dropout_prob
        _UpperCamelCase      :	str						=       max_position_embeddings
        _UpperCamelCase      :	int						=       eos_token_id
        _UpperCamelCase      :	Dict						=       pad_token_id
        _UpperCamelCase      :	Optional[Any]						=       bos_token_id
        _UpperCamelCase      :	str						=       attention_window

        # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
        # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
        # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
        # because its local attention only attends to `self.attention_window` and one before and one after
        _UpperCamelCase      :	List[str]						=       self.attention_window + 2

        # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
        # the `test_attention_outputs` and `test_hidden_states_output` tests
        _UpperCamelCase      :	int						=       (
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				str:
        _UpperCamelCase      :	Tuple						=       ids_tensor([self.batch_size, self.seq_length - 1]					,   self.vocab_size	)
        _UpperCamelCase      :	Optional[Any]						=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)					,   1	)
        _UpperCamelCase      :	Tuple						=       tf.concat([input_ids, eos_tensor]					,   axis=1	)

        _UpperCamelCase      :	Tuple						=       ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

        _UpperCamelCase      :	List[Any]						=       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					,   attention_window=self.attention_window					,   **self.config_updates					,   )
        _UpperCamelCase      :	Dict						=       prepare_led_inputs_dict(__a					,   __a					,   __a	)
        _UpperCamelCase      :	Union[str, Any]						=       tf.concat(
            [tf.zeros_like(__a	)[:, :-1], tf.ones_like(__a	)[:, -1:]]					,   axis=-1					,   )
        _UpperCamelCase      :	Union[str, Any]						=       global_attention_mask
        return config, inputs_dict


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       List[Any]					,   __a   :       int	)		->				Tuple:
        _UpperCamelCase      :	Tuple						=       TFLEDModel(config=__a	).get_decoder()
        _UpperCamelCase      :	Tuple						=       inputs_dict["input_ids"]

        _UpperCamelCase      :	int						=       input_ids[:1, :]
        _UpperCamelCase      :	List[str]						=       inputs_dict["attention_mask"][:1, :]
        _UpperCamelCase      :	List[Any]						=       1

        # first forward pass
        _UpperCamelCase      :	Any						=       model(__a					,   attention_mask=__a					,   use_cache=__a	)

        _UpperCamelCase,  _UpperCamelCase      :	Union[str, Any]						=       outputs.to_tuple()

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

        # append to next input_ids and
        _UpperCamelCase      :	List[str]						=       tf.concat([input_ids, next_tokens]					,   axis=-1	)
        _UpperCamelCase      :	Union[str, Any]						=       tf.concat([attention_mask, next_attn_mask]					,   axis=-1	)

        _UpperCamelCase      :	Tuple						=       model(__a					,   attention_mask=__a	)[0]
        _UpperCamelCase      :	int						=       model(__a					,   attention_mask=__a					,   past_key_values=__a	)[0]

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

        # select random slice
        _UpperCamelCase      :	List[Any]						=       int(ids_tensor((1,)					,   output_from_past.shape[-1]	)	)
        _UpperCamelCase      :	List[str]						=       output_from_no_past[:, -3:, random_slice_idx]
        _UpperCamelCase      :	Optional[int]						=       output_from_past[:, :, random_slice_idx]

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





def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_=None		,lowercase_=None		,)					->		Dict:




      """simple docstring"""

      if attention_mask is None:
            _UpperCamelCase      :	str						=       tf.cast(tf.math.not_equal(lowercase_		,config.pad_token_id			)		,tf.inta			)
      if decoder_attention_mask is None:
            _UpperCamelCase      :	str						=       tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape		,dtype=tf.inta			),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,config.pad_token_id			)		,tf.inta			),
                ]		,axis=-1		,)
      if head_mask is None:
            _UpperCamelCase      :	List[str]						=       tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            _UpperCamelCase      :	List[str]						=       tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "decoder_input_ids": decoder_input_ids,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
      }



@require_tf
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  _UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any	     =					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__							:List[str]	     =					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__							:List[str]	     =					(
      {
          "conversational": TFLEDForConditionalGeneration,
          "feature-extraction": TFLEDModel,
          "summarization": TFLEDForConditionalGeneration,
          "text2text-generation": TFLEDForConditionalGeneration,
          "translation": TFLEDForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__							:Tuple	     =					True
  SCREAMING_SNAKE_CASE__							:str	     =					False
  SCREAMING_SNAKE_CASE__							:Optional[Any]	     =					False
  SCREAMING_SNAKE_CASE__							:int	     =					False

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				List[Any]:
        _UpperCamelCase      :	int						=       TFLEDModelTester(self	)
        _UpperCamelCase      :	Any						=       ConfigTester(self					,   config_class=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Optional[Any]:
        self.config_tester.run_common_tests()

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				int:
        _UpperCamelCase      :	Tuple						=       self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				List[str]:
        _UpperCamelCase,  _UpperCamelCase      :	List[Any]						=       self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase      :	Optional[int]						=       tf.zeros_like(inputs_dict["attention_mask"]	)
        _UpperCamelCase      :	Union[str, Any]						=       2
        _UpperCamelCase      :	str						=       tf.where(
            tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices					,   1					,   inputs_dict["global_attention_mask"]					,   )

        _UpperCamelCase      :	Dict						=       True
        _UpperCamelCase      :	str						=       self.model_tester.seq_length
        _UpperCamelCase      :	Union[str, Any]						=       self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(__a   :       Optional[int]	):
              _UpperCamelCase      :	Optional[int]						=       outputs.decoder_attentions
              self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
              self.assertListEqual(
                  list(decoder_attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, seq_length, seq_length]					,   )

        def check_encoder_attentions_output(__a   :       Optional[Any]	):
              _UpperCamelCase      :	Union[str, Any]						=       [t.numpy() for t in outputs.encoder_attentions]
              _UpperCamelCase      :	List[Any]						=       [t.numpy() for t in outputs.encoder_global_attentions]
              self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
              self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
              self.assertListEqual(
                  list(attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, seq_length, seq_length]					,   )
              self.assertListEqual(
                  list(global_attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]					,   )

        for model_class in self.all_model_classes:
              _UpperCamelCase      :	Dict						=       True
              _UpperCamelCase      :	Optional[Any]						=       False
              _UpperCamelCase      :	int						=       False
              _UpperCamelCase      :	Optional[int]						=       model_class(__a	)
              _UpperCamelCase      :	int						=       model(self._prepare_for_class(__a					,   __a	)	)
              _UpperCamelCase      :	Any						=       len(__a	)
              self.assertEqual(config.output_hidden_states					,   __a	)
              check_encoder_attentions_output(__a	)

              if self.is_encoder_decoder:
                    _UpperCamelCase      :	Optional[Any]						=       model_class(__a	)
                    _UpperCamelCase      :	List[Any]						=       model(self._prepare_for_class(__a					,   __a	)	)
                    self.assertEqual(config.output_hidden_states					,   __a	)
                    check_decoder_attentions_output(__a	)

              # Check that output attentions can also be changed via the config
              del inputs_dict["output_attentions"]
              _UpperCamelCase      :	int						=       True
              _UpperCamelCase      :	Tuple						=       model_class(__a	)
              _UpperCamelCase      :	str						=       model(self._prepare_for_class(__a					,   __a	)	)
              self.assertEqual(config.output_hidden_states					,   __a	)
              check_encoder_attentions_output(__a	)

              # Check attention is always last and order is fine
              _UpperCamelCase      :	Any						=       True
              _UpperCamelCase      :	List[str]						=       True
              _UpperCamelCase      :	Tuple						=       model_class(__a	)
              _UpperCamelCase      :	int						=       model(self._prepare_for_class(__a					,   __a	)	)

              self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)					,   len(__a	)	)
              self.assertEqual(model.config.output_hidden_states					,   __a	)
              check_encoder_attentions_output(__a	)

  @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Dict:
        pass


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Tuple:
        # TODO: Head-masking not yet implement
        pass





def 					lowercase__    (					lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      return tf.constant(lowercase_		,dtype=tf.intaa			)


lowerCamelCase__				  =   1E-4



@slow
@require_tf
class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Union[str, Any]:
        _UpperCamelCase      :	Any						=       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

        # change to intended input here
        _UpperCamelCase      :	int						=       _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	Tuple						=       _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	Optional[Any]						=       prepare_led_inputs_dict(model.config					,   __a					,   __a	)
        _UpperCamelCase      :	Optional[int]						=       model(**__a	)[0]
        _UpperCamelCase      :	Optional[int]						=       (1, 1024, 768)
        self.assertEqual(output.shape					,   __a	)
        # change to expected output here
        _UpperCamelCase      :	Tuple						=       tf.convert_to_tensor(
            [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]					,   )
        tf.debugging.assert_near(output[:, :3, :3]					,   __a					,   atol=1e-3	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				str:
        _UpperCamelCase      :	Optional[int]						=       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

        # change to intended input here
        _UpperCamelCase      :	Optional[int]						=       _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	List[str]						=       _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	Optional[Any]						=       prepare_led_inputs_dict(model.config					,   __a					,   __a	)
        _UpperCamelCase      :	Union[str, Any]						=       model(**__a	)[0]
        _UpperCamelCase      :	int						=       (1, 1024, model.config.vocab_size)
        self.assertEqual(output.shape					,   __a	)
        # change to expected output here
        _UpperCamelCase      :	Optional[int]						=       tf.convert_to_tensor(
            [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]					,   )
        tf.debugging.assert_near(output[:, :3, :3]					,   __a					,   atol=1e-3					,   rtol=1e-3	)
