
import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def   lowerCAmelCase_						(					)				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
	__magic_name__		:		Optional[int]	   =     Image.open(requests.get(_snake_case      ,   stream=_snake_case   ).raw   ).convert("RGB"   )

	return image






def   lowerCAmelCase_						(					_snake_case      :  List[Any]   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		str	   =     []
	# fmt: off

	# vision encoder
	rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")   )
	rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")   )
	rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")   )
	rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")   )
	rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")   )
	rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")   )

	for i in range(config.vision_config.num_hidden_layers   ):
		rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')   )
		rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')   )

	# QFormer
	rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight")   )
	rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias")   )

	# fmt: on
	return rename_keys






def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  int      ,   _snake_case      :  str   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		str	   =     dct.pop(_snake_case   )
	__magic_name__		:		Any	   =     val






def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  int   )				->		List[Any]:







	'''simple docstring'''


	for i in range(config.vision_config.num_hidden_layers   ):
		# read in original q and v biases
		__magic_name__		:		Union[str, Any]	   =     state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''   )
		__magic_name__		:		Dict	   =     state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''   )

		# next, set bias in the state dict
		__magic_name__		:		List[Any]	   =     torch.cat((q_bias, torch.zeros_like(_snake_case      ,   requires_grad=_snake_case   ), v_bias)   )
		__magic_name__		:		Any	   =     qkv_bias






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		int	   =     364 if "coco" in model_name else 224
	__magic_name__		:		Dict	   =     InstructBlipVisionConfig(image_size=_snake_case   ).to_dict()

	# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
	# seems like flan-T5 models don't have bos_token_id properly set?
	if "t5-xl" in model_name:
		__magic_name__		:		Any	   =     TaConfig.from_pretrained("google/flan-t5-xl"      ,   dense_act_fn="gelu"      ,   bos_token_id=1   ).to_dict()
	elif "t5-xxl" in model_name:
		__magic_name__		:		str	   =     TaConfig.from_pretrained("google/flan-t5-xxl"      ,   dense_act_fn="gelu"      ,   bos_token_id=1   ).to_dict()
	elif "vicuna-7b" in model_name:
		__magic_name__		:		Union[str, Any]	   =     LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"      ,   vocab_size=32001   ).to_dict()
	elif "vicuna-13b" in model_name:
		__magic_name__		:		List[Any]	   =     LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"      ,   vocab_size=32001   ).to_dict()
	else:
		raise ValueError("Model name not supported"   )

	# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
	__magic_name__		:		List[Any]	   =     InstructBlipQFormerConfig(vocab_size=30523   ).to_dict()
	__magic_name__		:		str	   =     InstructBlipConfig(vision_config=_snake_case      ,   text_config=_snake_case      ,   qformer_config=_snake_case   )

	return config, image_size






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  Dict=None      ,   _snake_case      :  Tuple=False   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Any	   =     AutoTokenizer.from_pretrained("bert-base-uncased"      ,   truncation_side="left"   )
	qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}   )

	if "t5" in model_name:
		__magic_name__		:		Optional[Any]	   =     TaTokenizerFast.from_pretrained("google/flan-t5-xl"      ,   truncation_side="left"   )
	elif "vicuna" in model_name:
		# the following was used in the original implementation:
		# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
		# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
		# tokenizer.add_special_tokens({"bos_token": "</s>"})
		# tokenizer.add_special_tokens({"eos_token": "</s>"})
		# tokenizer.add_special_tokens({"unk_token": "</s>"})
		__magic_name__		:		List[Any]	   =     LlamaTokenizerFast.from_pretrained(
		    "huggyllama/llama-7b"      ,   truncation_side="left"      ,   bos_token="</s>"      ,   unk_token="</s>"   )
		tokenizer.add_special_tokens({"pad_token": "[PAD]"}   )

	__magic_name__   ,    __magic_name__		:		Dict	   =     get_blipa_config(_snake_case   )
	__magic_name__		:		int	   =     InstructBlipForConditionalGeneration(_snake_case   ).eval()

	__magic_name__		:		Optional[int]	   =     {
	    "instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
	    "instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
	    "instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
	    "instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
	}

	__magic_name__   ,    __magic_name__		:		List[Any]	   =     model_name_to_original[model_name]

	# load original model
	print("Loading original model..."   )
	__magic_name__		:		str	   =     "cuda:1" if torch.cuda.is_available() else "cpu"
	__magic_name__		:		Optional[int]	   =     "cuda:2" if torch.cuda.is_available() else "cpu"
	__magic_name__   ,    __magic_name__   ,    __magic_name__		:		List[str]	   =     load_model_and_preprocess(
	    name=_snake_case      ,   model_type=_snake_case      ,   is_eval=_snake_case      ,   device=_snake_case   )
	original_model.eval()
	print("Done!"   )

	# update state dict keys
	__magic_name__		:		str	   =     original_model.state_dict()
	__magic_name__		:		Optional[int]	   =     create_rename_keys(_snake_case   )
	for src, dest in rename_keys:
		rename_key(_snake_case      ,   _snake_case      ,   _snake_case   )

	# some keys can be renamed efficiently
	for key, val in state_dict.copy().items():
		__magic_name__		:		int	   =     state_dict.pop(_snake_case   )
		if key.startswith("Qformer.bert"   ):
			__magic_name__		:		Dict	   =     key.replace("Qformer.bert"      ,   "qformer"   )
		if "attention.self" in key:
			__magic_name__		:		str	   =     key.replace("self"      ,   "attention"   )
		if "llm_proj" in key:
			__magic_name__		:		int	   =     key.replace("llm_proj"      ,   "language_projection"   )
		if "t5_proj" in key:
			__magic_name__		:		Dict	   =     key.replace("t5_proj"      ,   "language_projection"   )
		if key.startswith("llm_model"   ):
			__magic_name__		:		Optional[int]	   =     key.replace("llm_model"      ,   "language_model"   )
		if key.startswith("t5"   ):
			__magic_name__		:		Optional[int]	   =     key.replace("t5"      ,   "language"   )
		__magic_name__		:		Union[str, Any]	   =     val

	# read in qv biases
	read_in_q_v_bias(_snake_case      ,   _snake_case   )

	# note: weights get loaded in torch.float32 by default
	hf_model.load_state_dict(_snake_case      ,   strict=_snake_case   )

	__magic_name__		:		Union[str, Any]	   =     load_demo_image()
	__magic_name__		:		Tuple	   =     "What is unusual about this image?"

	# create processor
	__magic_name__		:		str	   =     BlipImageProcessor(
	    size={"height": image_size, "width": image_size}      ,   image_mean=_snake_case      ,   image_std=_snake_case   )
	__magic_name__		:		Tuple	   =     InstructBlipProcessor(
	    image_processor=_snake_case      ,   tokenizer=_snake_case      ,   qformer_tokenizer=_snake_case      ,   )
	__magic_name__		:		List[str]	   =     processor(images=_snake_case      ,   text=_snake_case      ,   return_tensors="pt"   ).to(_snake_case   )

	# make sure processor creates exact same pixel values
	__magic_name__		:		List[Any]	   =     vis_processors["eval"](_snake_case   ).unsqueeze(0   ).to(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     inputs.pixel_values
	assert torch.allclose(original_pixel_values.to(pixel_values.device   )      ,   _snake_case   )

	original_model.to(_snake_case   )
	hf_model.to(_snake_case   )
	with torch.no_grad():
		if "vicuna" in model_name:
			__magic_name__		:		Tuple	   =     original_model({"image": original_pixel_values, "text_input": [prompt]}   ).logits
			__magic_name__		:		List[Any]	   =     hf_model(**_snake_case   ).logits
		else:
			__magic_name__		:		int	   =     original_model(
			    {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}   ).logits
			__magic_name__		:		Dict	   =     tokenizer("\n"      ,   return_tensors="pt"   ).input_ids.to(_snake_case   )
			__magic_name__		:		Optional[Any]	   =     label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id      ,   -100   )
			__magic_name__		:		Optional[Any]	   =     hf_model(**_snake_case      ,   labels=_snake_case   ).logits

	print("First values of original logits:"      ,   original_logits[0, :3, :3]   )
	print("First values of HF logits:"      ,   logits[0, :3, :3]   )

	# assert values
	assert original_logits.shape == logits.shape
	__magic_name__		:		List[str]	   =     1E-4 if "vicuna" in model_name else 1E-5
	assert torch.allclose(original_logits.to(logits.device   )      ,   _snake_case      ,   atol=_snake_case   )
	print("Looks ok!"   )

	print("Generating with original model..."   )
	__magic_name__		:		Dict	   =     original_model.generate({"image": original_pixel_values, "prompt": prompt}      ,   num_beams=5   )

	# important: we need to cast the weights of the HF model to the appropriate type
	print("Generating with HF model..."   )
	__magic_name__		:		Optional[int]	   =     hf_model.generate(
	    **_snake_case      ,   do_sample=_snake_case      ,   num_beams=5      ,   max_length=256      ,   min_length=1      ,   top_p=0.9      ,   repetition_penalty=1.5      ,   length_penalty=1.0      ,   temperature=1      ,   )
	if "vicuna" in model_name:
		# convert output id 0 to 2 (eos_token_id)
		# TODO add this in the generate method?
		__magic_name__		:		Tuple	   =     2
	print("Original generation:"      ,   _snake_case   )
	__magic_name__		:		Dict	   =     processor.batch_decode(_snake_case      ,   skip_special_tokens=_snake_case   )
	__magic_name__		:		List[str]	   =     [text.strip() for text in output_text]
	print("HF generation:"      ,   _snake_case   )

	if pytorch_dump_folder_path is not None:
		processor.save_pretrained(_snake_case   )
		hf_model.save_pretrained(_snake_case   )

	if push_to_hub:
		processor.push_to_hub(F'''Salesforce/{model_name}'''   )
		hf_model.push_to_hub(F'''Salesforce/{model_name}'''   )


if __name__ == "__main__":
				snake_case :		Union[str, Any]							      =  argparse.ArgumentParser()
				snake_case :		Tuple							      =  [
				    "instructblip-vicuna-7b",
				    "instructblip-vicuna-13b",
				    "instructblip-flan-t5-xl",
				    "instructblip-flan-t5-xxl",
				]
				parser.add_argument(
				    "--model_name",
				    default="instructblip-flan-t5-xl",
				    choices=choices,
				    type=str,
				    help="Path to hf config.json of model to convert",
				)
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument(
				    "--push_to_hub",
				    action="store_true",
				    help="Whether to push the model and processor to the hub after converting",
				)

				snake_case :		Optional[int]							      =  parser.parse_args()

				convert_blipa_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       _snake_case					:
			UpperCamelCase__          =    LEDConfig
			UpperCamelCase__          =    {}
			UpperCamelCase__          =    'gelu'







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

				__magic_name__		:		Any	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		List[str]	   =     max_position_embeddings
				__magic_name__		:		Any	   =     eos_token_id
				__magic_name__		:		str	   =     pad_token_id
				__magic_name__		:		int	   =     bos_token_id
				__magic_name__		:		Optional[int]	   =     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
				__magic_name__		:		Tuple	   =     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
				__magic_name__		:		Tuple	   =     (
				    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
				)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     ids_tensor([self.batch_size, self.seq_length - 1]						,   self.vocab_size		)
				__magic_name__		:		int	   =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)						,   1		)
				__magic_name__		:		int	   =     tf.concat([input_ids, eos_tensor]						,   axis=1		)

				__magic_name__		:		Optional[int]	   =     ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size		)

				__magic_name__		:		Dict	   =     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						,   )
				__magic_name__		:		List[str]	   =     prepare_led_inputs_dict(_a						,   _a						,   _a		)
				__magic_name__		:		Union[str, Any]	   =     tf.concat(
				    [tf.zeros_like(_a		)[:, :-1], tf.ones_like(_a		)[:, -1:]]						,   axis=-1						,   )
				__magic_name__		:		List[Any]	   =     global_attention_mask
				return config, inputs_dict







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				__magic_name__		:		Dict	   =     TFLEDModel(config=_a		).get_decoder()
				__magic_name__		:		Optional[int]	   =     inputs_dict["input_ids"]

				__magic_name__		:		Union[str, Any]	   =     input_ids[:1, :]
				__magic_name__		:		str	   =     inputs_dict["attention_mask"][:1, :]
				__magic_name__		:		int	   =     1

				# first forward pass
				__magic_name__		:		Tuple	   =     model(_a						,   attention_mask=_a						,   use_cache=_a		)

				__magic_name__   ,    __magic_name__		:		str	   =     outputs.to_tuple()

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

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

				__magic_name__		:		List[str]	   =     model(_a						,   attention_mask=_a		)[0]
				__magic_name__		:		Dict	   =     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
				__magic_name__		:		List[Any]	   =     int(ids_tensor((1,)						,   output_from_past.shape[-1]		)		)
				__magic_name__		:		Union[str, Any]	   =     output_from_no_past[:, -3:, random_slice_idx]
				__magic_name__		:		List[str]	   =     output_from_past[:, :, random_slice_idx]

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







def   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Any      ,   _snake_case      :  str=None      ,   _snake_case      :  List[str]=None      ,   _snake_case      :  int=None      ,   _snake_case      :  Any=None      ,   )				->		int:







	'''simple docstring'''


	if attention_mask is None:
		__magic_name__		:		str	   =     tf.cast(tf.math.not_equal(_snake_case      ,   config.pad_token_id   )      ,   tf.inta   )
	if decoder_attention_mask is None:
		__magic_name__		:		List[Any]	   =     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:
		__magic_name__		:		Dict	   =     tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
	if decoder_head_mask is None:
		__magic_name__		:		Any	   =     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       _snake_case					(     snake_case				,						snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
			UpperCamelCase__          =    (TFLEDForConditionalGeneration,) if is_tf_available() else ()
			UpperCamelCase__          =    (
			    {
			        'conversational': TFLEDForConditionalGeneration,
			        'feature-extraction': TFLEDModel,
			        'summarization': TFLEDForConditionalGeneration,
			        'text2text-generation': TFLEDForConditionalGeneration,
			        'translation': TFLEDForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCamelCase__          =    True
			UpperCamelCase__          =    False
			UpperCamelCase__          =    False
			UpperCamelCase__          =    False







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     TFLEDModelTester(self		)
				__magic_name__		:		List[Any]	   =     ConfigTester(self						,   config_class=_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self.config_tester.run_common_tests()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__   ,    __magic_name__		:		int	   =     self.model_tester.prepare_config_and_inputs_for_common()
				__magic_name__		:		List[str]	   =     tf.zeros_like(inputs_dict["attention_mask"]		)
				__magic_name__		:		Optional[Any]	   =     2
				__magic_name__		:		Tuple	   =     tf.where(
				    tf.range(self.model_tester.seq_length		)[None, :] < num_global_attn_indices						,   1						,   inputs_dict["global_attention_mask"]						,   )

				__magic_name__		:		Any	   =     True
				__magic_name__		:		str	   =     self.model_tester.seq_length
				__magic_name__		:		Dict	   =     self.model_tester.encoder_seq_length

				def check_decoder_attentions_output(_a		):
					__magic_name__		:		str	   =     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		):
					__magic_name__		:		Any	   =     [t.numpy() for t in outputs.encoder_attentions]
					__magic_name__		:		Tuple	   =     [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:
					__magic_name__		:		Union[str, Any]	   =     True
					__magic_name__		:		List[str]	   =     False
					__magic_name__		:		Tuple	   =     False
					__magic_name__		:		Optional[int]	   =     model_class(_a		)
					__magic_name__		:		str	   =     model(self._prepare_for_class(_a						,   _a		)		)
					__magic_name__		:		Any	   =     len(_a		)
					self.assertEqual(config.output_hidden_states						,   _a		)
					check_encoder_attentions_output(_a		)

					if self.is_encoder_decoder:
						__magic_name__		:		Tuple	   =     model_class(_a		)
						__magic_name__		:		Optional[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"]
					__magic_name__		:		Dict	   =     True
					__magic_name__		:		str	   =     model_class(_a		)
					__magic_name__		:		Any	   =     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
					__magic_name__		:		Union[str, Any]	   =     True
					__magic_name__		:		Union[str, Any]	   =     True
					__magic_name__		:		List[str]	   =     model_class(_a		)
					__magic_name__		:		Any	   =     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		):
				pass







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# TODO: Head-masking not yet implement
				pass







def   lowerCAmelCase_						(					_snake_case      :  int   )				->		Optional[int]:







	'''simple docstring'''


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


snake_case :		Optional[int]							      =  1E-4





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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"		).led

				# change to intended input here
				__magic_name__		:		Optional[int]	   =     _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
				__magic_name__		:		str	   =     _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
				__magic_name__		:		Any	   =     prepare_led_inputs_dict(model.config						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     model(**_a		)[0]
				__magic_name__		:		List[str]	   =     (1, 1_024, 768)
				self.assertEqual(output.shape						,   _a		)
				# change to expected output here
				__magic_name__		:		int	   =     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		):
				__magic_name__		:		Tuple	   =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"		)

				# change to intended input here
				__magic_name__		:		int	   =     _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
				__magic_name__		:		Tuple	   =     _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
				__magic_name__		:		Optional[Any]	   =     prepare_led_inputs_dict(model.config						,   _a						,   _a		)
				__magic_name__		:		Union[str, Any]	   =     model(**_a		)[0]
				__magic_name__		:		Optional[int]	   =     (1, 1_024, model.config.vocab_size)
				self.assertEqual(output.shape						,   _a		)
				# change to expected output here
				__magic_name__		:		str	   =     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		)
