import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase   :				Any     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict					):
					SCREAMING_SNAKE_CASE           =    SwinConfig(
					    embed_dim=1_9_2 ,	depths=(2, 2, 1_8, 2) ,	num_heads=(6, 1_2, 2_4, 4_8) ,	window_size=1_2 ,	out_features=["stage2", "stage3", "stage4"] ,	)

					SCREAMING_SNAKE_CASE           =    DetaConfig(
					    backbone_config=UpperCAmelCase__ ,	num_queries=9_0_0 ,	encoder_ffn_dim=2_0_4_8 ,	decoder_ffn_dim=2_0_4_8 ,	num_feature_levels=5 ,	assign_first_stage=UpperCAmelCase__ ,	with_box_refine=UpperCAmelCase__ ,	two_stage=UpperCAmelCase__ ,	)

					# set labels
					SCREAMING_SNAKE_CASE           =    "huggingface/label-files"
					if "o365" in model_name:
										SCREAMING_SNAKE_CASE           =    3_6_6
										SCREAMING_SNAKE_CASE           =    "object365-id2label.json"
					else:
										SCREAMING_SNAKE_CASE           =    9_1
										SCREAMING_SNAKE_CASE           =    "coco-detection-id2label.json"

					SCREAMING_SNAKE_CASE           =    num_labels
					SCREAMING_SNAKE_CASE           =    json.load(open(cached_download(hf_hub_url(UpperCAmelCase__ ,	UpperCAmelCase__ ,	repo_type="dataset"					)					) ,	"r"					)					)
					SCREAMING_SNAKE_CASE           =    {int(UpperCAmelCase__					): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE           =    idalabel
					SCREAMING_SNAKE_CASE           =    {v: k for k, v in idalabel.items()}

					return config





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple					):
					SCREAMING_SNAKE_CASE           =    []

					# stem
					# fmt: off
					rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")					)
					rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")					)
					rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")					)
					rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")					)
					# stages
					for i in range(len(config.backbone_config.depths					)					):
										for j in range(config.backbone_config.depths[i]					):
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")					)

										if i < 3:
															rename_keys.append((F"backbone.0.body.layers.{i}.downsample.reduction.weight", F"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.weight", F"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")					)
															rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.bias", F"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")					)

					rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")					)
					rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")					)
					rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")					)
					rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")					)
					rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")					)
					rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")					)

					# transformer encoder
					for i in range(config.encoder_layers					):
										rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", F"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", F"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", F"model.encoder.layers.{i}.self_attn.attention_weights.weight")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", F"model.encoder.layers.{i}.self_attn.attention_weights.bias")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.weight", F"model.encoder.layers.{i}.self_attn.value_proj.weight")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.bias", F"model.encoder.layers.{i}.self_attn.value_proj.bias")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.weight", F"model.encoder.layers.{i}.self_attn.output_proj.weight")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.bias", F"model.encoder.layers.{i}.self_attn.output_proj.bias")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.norm1.weight", F"model.encoder.layers.{i}.self_attn_layer_norm.weight")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"model.encoder.layers.{i}.self_attn_layer_norm.bias")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"model.encoder.layers.{i}.fc1.weight")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"model.encoder.layers.{i}.fc1.bias")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"model.encoder.layers.{i}.fc2.weight")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"model.encoder.layers.{i}.fc2.bias")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"model.encoder.layers.{i}.final_layer_norm.weight")					)
										rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"model.encoder.layers.{i}.final_layer_norm.bias")					)

					# transformer decoder
					for i in range(config.decoder_layers					):
										rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", F"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", F"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", F"model.decoder.layers.{i}.encoder_attn.value_proj.weight")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", F"model.decoder.layers.{i}.encoder_attn.value_proj.bias")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", F"model.decoder.layers.{i}.encoder_attn.output_proj.weight")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", F"model.decoder.layers.{i}.encoder_attn.output_proj.bias")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.norm1.weight", F"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"model.decoder.layers.{i}.self_attn.out_proj.weight")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"model.decoder.layers.{i}.self_attn.out_proj.bias")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.norm2.weight", F"model.decoder.layers.{i}.self_attn_layer_norm.weight")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.norm2.bias", F"model.decoder.layers.{i}.self_attn_layer_norm.bias")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"model.decoder.layers.{i}.fc1.weight")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"model.decoder.layers.{i}.fc1.bias")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"model.decoder.layers.{i}.fc2.weight")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"model.decoder.layers.{i}.fc2.bias")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"model.decoder.layers.{i}.final_layer_norm.weight")					)
										rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"model.decoder.layers.{i}.final_layer_norm.bias")					)

					# fmt: on

					return rename_keys





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[Any]					):
					SCREAMING_SNAKE_CASE           =    dct.pop(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    val





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Tuple					):
					SCREAMING_SNAKE_CASE           =    [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
					for i in range(len(backbone_config.depths					)					):
										SCREAMING_SNAKE_CASE           =    num_features[i]
										for j in range(backbone_config.depths[i]					):
															# fmt: off
															# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
															SCREAMING_SNAKE_CASE           =    state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"					)
															SCREAMING_SNAKE_CASE           =    state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"					)
															# next, add query, keys and values (in that order) to the state dict
															SCREAMING_SNAKE_CASE           =    in_proj_weight[:dim, :]
															SCREAMING_SNAKE_CASE           =    in_proj_bias[: dim]
															SCREAMING_SNAKE_CASE           =    in_proj_weight[
															    dim : dim * 2, :
															]
															SCREAMING_SNAKE_CASE           =    in_proj_bias[
															    dim : dim * 2
															]
															SCREAMING_SNAKE_CASE           =    in_proj_weight[
															    -dim :, :
															]
															SCREAMING_SNAKE_CASE           =    in_proj_bias[-dim :]
															# fmt: on





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							List[str]					):
					# transformer decoder self-attention layers
					SCREAMING_SNAKE_CASE           =    config.d_model
					for i in range(config.decoder_layers					):
										# read in weights + bias of input projection layer of self-attention
										SCREAMING_SNAKE_CASE           =    state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"					)
										SCREAMING_SNAKE_CASE           =    state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_bias"					)
										# next, add query, keys and values (in that order) to the state dict
										SCREAMING_SNAKE_CASE           =    in_proj_weight[:hidden_size, :]
										SCREAMING_SNAKE_CASE           =    in_proj_bias[:hidden_size]
										SCREAMING_SNAKE_CASE           =    in_proj_weight[
										    hidden_size : hidden_size * 2, :
										]
										SCREAMING_SNAKE_CASE           =    in_proj_bias[hidden_size : hidden_size * 2]
										SCREAMING_SNAKE_CASE           =    in_proj_weight[-hidden_size:, :]
										SCREAMING_SNAKE_CASE           =    in_proj_bias[-hidden_size:]





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    "http://images.cocodataset.org/val2017/000000039769.jpg"
					SCREAMING_SNAKE_CASE           =    Image.open(requests.get(UpperCAmelCase__ ,	stream=UpperCAmelCase__					).raw					)

					return im





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							List[Any]					):
					SCREAMING_SNAKE_CASE           =    get_deta_config(UpperCAmelCase__					)

					# load original state dict
					if model_name == "deta-swin-large":
										SCREAMING_SNAKE_CASE           =    hf_hub_download(repo_id="nielsr/deta-checkpoints" ,	filename="adet_swin_ft.pth"					)
					elif model_name == "deta-swin-large-o365":
										SCREAMING_SNAKE_CASE           =    hf_hub_download(repo_id="jozhang97/deta-swin-l-o365" ,	filename="deta_swin_pt_o365.pth"					)
					else:
										raise ValueError(F"Model name {model_name} not supported"					)

					SCREAMING_SNAKE_CASE           =    torch.load(UpperCAmelCase__ ,	map_location="cpu"					)["model"]

					# original state dict
					for name, param in state_dict.items():
										print(UpperCAmelCase__ ,	param.shape					)

					# rename keys
					SCREAMING_SNAKE_CASE           =    create_rename_keys(UpperCAmelCase__					)
					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
					read_in_swin_q_k_v(UpperCAmelCase__ ,	config.backbone_config					)
					read_in_decoder_q_k_v(UpperCAmelCase__ ,	UpperCAmelCase__					)

					# fix some prefixes
					for key in state_dict.copy().keys():
										if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
															SCREAMING_SNAKE_CASE           =    state_dict.pop(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    val
										if "input_proj" in key:
															SCREAMING_SNAKE_CASE           =    state_dict.pop(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    val
										if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
															SCREAMING_SNAKE_CASE           =    state_dict.pop(UpperCAmelCase__					)
															SCREAMING_SNAKE_CASE           =    val

    # finally, create HuggingFace model and load state dict
					SCREAMING_SNAKE_CASE           =    DetaForObjectDetection(UpperCAmelCase__					)
					model.load_state_dict(UpperCAmelCase__					)
					model.eval()

					SCREAMING_SNAKE_CASE           =    "cuda" if torch.cuda.is_available() else "cpu"
					model.to(UpperCAmelCase__					)

					# load image processor
					SCREAMING_SNAKE_CASE           =    DetaImageProcessor(format="coco_detection"					)

					# verify our conversion on image
					SCREAMING_SNAKE_CASE           =    prepare_img()
					SCREAMING_SNAKE_CASE           =    processor(images=UpperCAmelCase__ ,	return_tensors="pt"					)
					SCREAMING_SNAKE_CASE           =    encoding["pixel_values"]
					SCREAMING_SNAKE_CASE           =    model(pixel_values.to(UpperCAmelCase__					)					)

					# verify logits
					print("Logits:" ,	outputs.logits[0, :3, :3]					)
					print("Boxes:" ,	outputs.pred_boxes[0, :3, :3]					)
					if model_name == "deta-swin-large":
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]					)
										SCREAMING_SNAKE_CASE           =    torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]					)
					elif model_name == "deta-swin-large-o365":
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]					)
										SCREAMING_SNAKE_CASE           =    torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]					)
					assert torch.allclose(outputs.logits[0, :3, :3] ,	expected_logits.to(UpperCAmelCase__					) ,	atol=1e-4					)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,	expected_boxes.to(UpperCAmelCase__					) ,	atol=1e-4					)
					print("Everything ok!"					)

					if pytorch_dump_folder_path:
										# Save model and processor
										logger.info(F"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."					)
										Path(UpperCAmelCase__					).mkdir(exist_ok=UpperCAmelCase__					)
										model.save_pretrained(UpperCAmelCase__					)
										processor.save_pretrained(UpperCAmelCase__					)

					# Push to hub
					if push_to_hub:
										print("Pushing model and processor to hub..."					)
										model.push_to_hub(F"jozhang97/{model_name}"					)
										processor.push_to_hub(F"jozhang97/{model_name}"					)


if __name__ == "__main__":
							_lowerCamelCase   :				Optional[Any]     			=						argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''',
							    type=str,
							    default='''deta-swin-large''',
							    choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
							    help='''Name of the model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    help='''Path to the folder to output PyTorch model.''',
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)
							_lowerCamelCase   :				Any     			=						parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





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   lowercase      :
							lowercase__  :    Dict									=							LEDConfig
							lowercase__  :    List[str]									=							{}
							lowercase__  :    Union[str, Any]									=							"""gelu"""




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Dict=13		,							_UpperCamelCase				:   Optional[int]=7		,							_UpperCamelCase				:   int=True		,							_UpperCamelCase				:   List[Any]=False		,							_UpperCamelCase				:   Dict=99		,							_UpperCamelCase				:   Optional[Any]=32		,							_UpperCamelCase				:   Any=2		,							_UpperCamelCase				:   List[str]=4		,							_UpperCamelCase				:   Union[str, Any]=37		,							_UpperCamelCase				:   str=0.1		,							_UpperCamelCase				:   List[Any]=0.1		,							_UpperCamelCase				:   Union[str, Any]=20		,							_UpperCamelCase				:   str=2		,							_UpperCamelCase				:   Optional[Any]=1		,							_UpperCamelCase				:   Optional[int]=0		,							_UpperCamelCase				:   int=4		,							)   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    seq_length
												SCREAMING_SNAKE_CASE           =    is_training
												SCREAMING_SNAKE_CASE           =    use_labels
												SCREAMING_SNAKE_CASE           =    vocab_size
												SCREAMING_SNAKE_CASE           =    hidden_size
												SCREAMING_SNAKE_CASE           =    num_hidden_layers
												SCREAMING_SNAKE_CASE           =    num_attention_heads
												SCREAMING_SNAKE_CASE           =    intermediate_size

												SCREAMING_SNAKE_CASE           =    hidden_dropout_prob
												SCREAMING_SNAKE_CASE           =    attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE           =    max_position_embeddings
												SCREAMING_SNAKE_CASE           =    eos_token_id
												SCREAMING_SNAKE_CASE           =    pad_token_id
												SCREAMING_SNAKE_CASE           =    bos_token_id
												SCREAMING_SNAKE_CASE           =    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
												SCREAMING_SNAKE_CASE           =    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
												SCREAMING_SNAKE_CASE           =    (
												    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
												)




							def 		__snake_case(  self				:   int )   ->			Tuple:



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE           =    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		,							)
												SCREAMING_SNAKE_CASE           =    prepare_led_inputs_dict(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tf.concat(
												    [tf.zeros_like(_UpperCamelCase )[:, :-1], tf.ones_like(_UpperCamelCase )[:, -1:]]		,							axis=-1		,							)
												SCREAMING_SNAKE_CASE           =    global_attention_mask
												return config, inputs_dict





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Tuple )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDModel(config=_UpperCamelCase ).get_decoder()
												SCREAMING_SNAKE_CASE           =    inputs_dict["input_ids"]

												SCREAMING_SNAKE_CASE           =    input_ids[:1, :]
												SCREAMING_SNAKE_CASE           =    inputs_dict["attention_mask"][:1, :]
												SCREAMING_SNAKE_CASE           =    1

												# first forward pass
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							use_cache=_UpperCamelCase )

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    outputs.to_tuple()

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

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

												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							past_key_values=_UpperCamelCase )[0]

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

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

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							List[str]=None ,	UpperCAmelCase__       :							Union[str, Any]=None ,	UpperCAmelCase__       :							Optional[int]=None ,	UpperCAmelCase__       :							Union[str, Any]=None ,	):
					if attention_mask is None:
										SCREAMING_SNAKE_CASE           =    tf.cast(tf.math.not_equal(UpperCAmelCase__ ,	config.pad_token_id					) ,	tf.inta					)
					if decoder_attention_mask is None:
										SCREAMING_SNAKE_CASE           =    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:
										SCREAMING_SNAKE_CASE           =    tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
					if decoder_head_mask is None:
										SCREAMING_SNAKE_CASE           =    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   lowercase      (       a			,    a			,    unittest.TestCase       ):
							lowercase__  :    Optional[int]									=							(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
							lowercase__  :    List[Any]									=							(TFLEDForConditionalGeneration,) if is_tf_available() else ()
							lowercase__  :    int									=							(
							    {
							        """conversational""": TFLEDForConditionalGeneration,
							        """feature-extraction""": TFLEDModel,
							        """summarization""": TFLEDForConditionalGeneration,
							        """text2text-generation""": TFLEDForConditionalGeneration,
							        """translation""": TFLEDForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase__  :    List[Any]									=							True
							lowercase__  :    List[str]									=							False
							lowercase__  :    List[str]									=							False
							lowercase__  :    Union[str, Any]									=							False




							def 		__snake_case(  self				:   Tuple )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDModelTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							def 		__snake_case(  self				:   List[str] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase )




							def 		__snake_case(  self				:   Dict )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()
												SCREAMING_SNAKE_CASE           =    tf.zeros_like(inputs_dict["attention_mask"] )
												SCREAMING_SNAKE_CASE           =    2
												SCREAMING_SNAKE_CASE           =    tf.where(
												    tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices		,							1		,							inputs_dict["global_attention_mask"]		,							)

												SCREAMING_SNAKE_CASE           =    True
												SCREAMING_SNAKE_CASE           =    self.model_tester.seq_length
												SCREAMING_SNAKE_CASE           =    self.model_tester.encoder_seq_length

												def check_decoder_attentions_output(_UpperCamelCase				:   Dict ):
																	SCREAMING_SNAKE_CASE           =    outputs.decoder_attentions
																	self.assertEqual(len(_UpperCamelCase )		,							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(_UpperCamelCase				:   Optional[Any] ):
																	SCREAMING_SNAKE_CASE           =    [t.numpy() for t in outputs.encoder_attentions]
																	SCREAMING_SNAKE_CASE           =    [t.numpy() for t in outputs.encoder_global_attentions]
																	self.assertEqual(len(_UpperCamelCase )		,							self.model_tester.num_hidden_layers )
																	self.assertEqual(len(_UpperCamelCase )		,							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:
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    False
																	SCREAMING_SNAKE_CASE           =    False
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    model(self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )
																	SCREAMING_SNAKE_CASE           =    len(_UpperCamelCase )
																	self.assertEqual(config.output_hidden_states		,							_UpperCamelCase )
																	check_encoder_attentions_output(_UpperCamelCase )

																	if self.is_encoder_decoder:
																						SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    model(self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )
																						self.assertEqual(config.output_hidden_states		,							_UpperCamelCase )
																						check_decoder_attentions_output(_UpperCamelCase )

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

																	# Check attention is always last and order is fine
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    True
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    model(self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )

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




							@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
							def 		__snake_case(  self				:   Optional[Any] )   ->			Tuple:



												'''simple docstring'''

												pass





							def 		__snake_case(  self				:   str )   ->			str:



												'''simple docstring'''

												pass





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int]					):
					return tf.constant(UpperCAmelCase__ ,	dtype=tf.intaa					)


_lowerCamelCase   :				str     			=						1e-4


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




							def 		__snake_case(  self				:   int )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led

												# change to intended input here
												SCREAMING_SNAKE_CASE           =    _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    prepare_led_inputs_dict(model.config		,							_UpperCamelCase		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    (1, 1_024, 768)
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# change to expected output here
												SCREAMING_SNAKE_CASE           =    tf.convert_to_tensor(
												    [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]		,							)
												tf.debugging.assert_near(output[:, :3, :3]		,							_UpperCamelCase		,							atol=1e-3 )





							def 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )

												# change to intended input here
												SCREAMING_SNAKE_CASE           =    _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
												SCREAMING_SNAKE_CASE           =    prepare_led_inputs_dict(model.config		,							_UpperCamelCase		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    (1, 1_024, model.config.vocab_size)
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# change to expected output here
												SCREAMING_SNAKE_CASE           =    tf.convert_to_tensor(
												    [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]		,							)
												tf.debugging.assert_near(output[:, :3, :3]		,							_UpperCamelCase		,							atol=1e-3		,							rtol=1e-3 )




