import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase:	Dict       			=		logging.get_logger(__name__)






def _lowerCamelCase  (		snake_case     , snake_case=False  ):
							_lowerCAmelCase							=							[]

							# fmt: off
							# stem:
							rename_keys.append(('cls_token', 'vit.embeddings.cls_token')  )
							rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')  )

							rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')  )
							rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')  )

							# backbone
							rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')  )
							rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')  )
							rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')  )

							for stage_idx in range(len(config.backbone_config.depths  )  ):
														for layer_idx in range(config.backbone_config.depths[stage_idx]  ):
																					rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')  )
																					rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')  )
																					rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')  )
																					rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')  )
																					rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')  )
																					rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')  )
																					rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')  )
																					rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')  )
																					rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')  )

														rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')  )
														rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')  )
														rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')  )

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

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('norm.weight', 'layernorm.weight'),
														        ('norm.bias', 'layernorm.bias'),
														        ('pre_logits.fc.weight', 'pooler.dense.weight'),
														        ('pre_logits.fc.bias', 'pooler.dense.bias'),
														    ]  )

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														_lowerCAmelCase							=							[(pair[0], pair[1][4:]) if pair[1].startswith('vit'  ) else pair for pair in rename_keys]
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ('norm.weight', 'vit.layernorm.weight'),
														        ('norm.bias', 'vit.layernorm.bias'),
														        ('head.weight', 'classifier.weight'),
														        ('head.bias', 'classifier.bias'),
														    ]  )
							# fmt: on

							return rename_keys






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case=False  ):
							for i in range(config.num_hidden_layers  ):
														if base_model:
																					_lowerCAmelCase							=							''
														else:
																					_lowerCAmelCase							=							'vit.'
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														_lowerCAmelCase							=							state_dict.pop(F'blocks.{i}.attn.qkv.weight'  )
														_lowerCAmelCase							=							state_dict.pop(F'blocks.{i}.attn.qkv.bias'  )
														# next, add query, keys and values (in that order) to the state dict
														_lowerCAmelCase							=							in_proj_weight[
														    : config.hidden_size, :
														]
														_lowerCAmelCase							=							in_proj_bias[: config.hidden_size]
														_lowerCAmelCase							=							in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														_lowerCAmelCase							=							in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														_lowerCAmelCase							=							in_proj_weight[
														    -config.hidden_size :, :
														]
														_lowerCAmelCase							=							in_proj_bias[-config.hidden_size :]






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							['head.weight', 'head.bias']
							for k in ignore_keys:
														state_dict.pop(snake_case     , snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case  ):
							_lowerCAmelCase							=							dct.pop(snake_case  )
							_lowerCAmelCase							=							val






def _lowerCamelCase  (		):
							_lowerCAmelCase							=							'http://images.cocodataset.org/val2017/000000039769.jpg'
							_lowerCAmelCase							=							Image.open(requests.get(snake_case     , stream=snake_case  ).raw  )
							return im






@torch.no_grad()
def _lowerCamelCase  (		snake_case     , snake_case     , snake_case=False  ):
							_lowerCAmelCase							=							BitConfig(
							    global_padding='same'     , layer_type='bottleneck'     , depths=(3, 4, 9)     , out_features=['stage3']     , embedding_dynamic_padding=snake_case     , )
							_lowerCAmelCase							=							ViTHybridConfig(backbone_config=snake_case     , image_size=384     , num_labels=1_000  )
							_lowerCAmelCase							=							False

							# load original model from timm
							_lowerCAmelCase							=							timm.create_model(snake_case     , pretrained=snake_case  )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							_lowerCAmelCase							=							timm_model.state_dict()
							if base_model:
														remove_classification_head_(snake_case  )
							_lowerCAmelCase							=							create_rename_keys(snake_case     , snake_case  )
							for src, dest in rename_keys:
														rename_key(snake_case     , snake_case     , snake_case  )
							read_in_q_k_v(snake_case     , snake_case     , snake_case  )

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

							# load HuggingFace model
							if vit_name[-5:] == "in21k":
														_lowerCAmelCase							=							ViTHybridModel(snake_case  ).eval()
							else:
														_lowerCAmelCase							=							ViTHybridForImageClassification(snake_case  ).eval()
							model.load_state_dict(snake_case  )

							# create image processor
							_lowerCAmelCase							=							create_transform(**resolve_data_config({}     , model=snake_case  )  )
							_lowerCAmelCase							=							transform.transforms

							_lowerCAmelCase							=							{
							    'bilinear': PILImageResampling.BILINEAR,
							    'bicubic': PILImageResampling.BICUBIC,
							    'nearest': PILImageResampling.NEAREST,
							}

							_lowerCAmelCase							=							ViTHybridImageProcessor(
							    do_resize=snake_case     , size={'shortest_edge': timm_transforms[0].size}     , resample=pillow_resamplings[timm_transforms[0].interpolation.value]     , do_center_crop=snake_case     , crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}     , do_normalize=snake_case     , image_mean=timm_transforms[-1].mean.tolist()     , image_std=timm_transforms[-1].std.tolist()     , )

							_lowerCAmelCase							=							prepare_img()
							_lowerCAmelCase							=							transform(snake_case  ).unsqueeze(0  )
							_lowerCAmelCase							=							processor(snake_case     , return_tensors='pt'  ).pixel_values

							# verify pixel values
							assert torch.allclose(snake_case     , snake_case  )

							# verify logits
							with torch.no_grad():
														_lowerCAmelCase							=							model(snake_case  )
														_lowerCAmelCase							=							outputs.logits

							print('Predicted class:'     , logits.argmax(-1  ).item()  )
							if base_model:
														_lowerCAmelCase							=							timm_model.forward_features(snake_case  )
														assert timm_pooled_output.shape == outputs.pooler_output.shape
														assert torch.allclose(snake_case     , outputs.pooler_output     , atol=1E-3  )
							else:
														_lowerCAmelCase							=							timm_model(snake_case  )
														assert timm_logits.shape == outputs.logits.shape
														assert torch.allclose(snake_case     , outputs.logits     , atol=1E-3  )
							print('Looks ok!'  )

							if pytorch_dump_folder_path is not None:
														Path(snake_case  ).mkdir(exist_ok=snake_case  )
														print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'  )
														model.save_pretrained(snake_case  )
														print(F'Saving processor to {pytorch_dump_folder_path}'  )
														processor.save_pretrained(snake_case  )

							if push_to_hub:
														print(F'Pushing model and processor to the hub {vit_name}'  )
														model.push_to_hub(F'ybelkada/{vit_name}'  )
														processor.push_to_hub(F'ybelkada/{vit_name}'  )


if __name__ == "__main__":
				_lowercase:	Any       			=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--vit_name''',
				    default='''vit_base_r50_s16_384''',
				    type=str,
				    help='''Name of the hybrid ViT timm model you\'d like to convert.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
				)

				_lowercase:	Tuple       			=		parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class        lowerCamelCase__				:





			def __init__(       self	:	Union[str, Any]  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Dict=99  ,  lowercase__	:	List[Any]=13  ,  lowercase__	:	Union[str, Any]=7  ,  lowercase__	:	int=9  ,  lowercase__	:	List[str]=True  ,  lowercase__	:	str=True  ,  lowercase__	:	Any=False  ,  lowercase__	:	int=32  ,  lowercase__	:	Any=5  ,  lowercase__	:	int=4  ,  lowercase__	:	int=37  ,  lowercase__	:	List[str]=8  ,  lowercase__	:	Optional[int]=0.1  ,  lowercase__	:	List[Any]=0.0_0_2  ,  lowercase__	:	str=1  ,  lowercase__	:	List[str]=0  ,  lowercase__	:	Optional[int]=0  ,  lowercase__	:	List[str]=None  ,  lowercase__	:	Any=None  ,  ):
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							encoder_seq_length
										_lowerCAmelCase							=							decoder_seq_length
										# For common tests
										_lowerCAmelCase							=							self.decoder_seq_length
										_lowerCAmelCase							=							is_training
										_lowerCAmelCase							=							use_attention_mask
										_lowerCAmelCase							=							use_labels
										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							d_ff
										_lowerCAmelCase							=							relative_attention_num_buckets
										_lowerCAmelCase							=							dropout_rate
										_lowerCAmelCase							=							initializer_factor
										_lowerCAmelCase							=							eos_token_id
										_lowerCAmelCase							=							pad_token_id
										_lowerCAmelCase							=							decoder_start_token_id
										_lowerCAmelCase							=							None
										_lowerCAmelCase							=							decoder_layers





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										return TaConfig.from_pretrained('google/umt5-base'						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	List[str]  ,  lowercase__	:	Any  ,  lowercase__	:	int  ,  lowercase__	:	int=None  ,  lowercase__	:	str=None  ,  lowercase__	:	int=None  ,  lowercase__	:	Optional[int]=None  ,  lowercase__	:	List[Any]=None  ,  ):
										if attention_mask is None:
																	_lowerCAmelCase							=							input_ids.ne(config.pad_token_id						)
										if decoder_attention_mask is None:
																	_lowerCAmelCase							=							decoder_input_ids.ne(config.pad_token_id						)
										if head_mask is None:
																	_lowerCAmelCase							=							torch.ones(config.num_hidden_layers  ,  config.num_attention_heads  ,  device=lowercase__						)
										if decoder_head_mask is None:
																	_lowerCAmelCase							=							torch.ones(config.num_decoder_layers  ,  config.num_attention_heads  ,  device=lowercase__						)
										if cross_attn_head_mask is None:
																	_lowerCAmelCase							=							torch.ones(
																	    config.num_decoder_layers  ,  config.num_attention_heads  ,  device=lowercase__						)
										return {
										    "input_ids": input_ids,
										    "decoder_input_ids": decoder_input_ids,
										    "attention_mask": attention_mask,
										    "decoder_attention_mask": decoder_attention_mask,
										    "head_mask": head_mask,
										    "decoder_head_mask": decoder_head_mask,
										    "cross_attn_head_mask": cross_attn_head_mask,
										}





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							ids_tensor([self.batch_size, self.encoder_seq_length]  ,  self.vocab_size						)
										_lowerCAmelCase							=							ids_tensor([self.batch_size, self.decoder_seq_length]  ,  self.vocab_size						)

										# we need to clamp the input ids here to avoid having pad token in between
										# this is because for NllbMoe the position_ids are prepared such that
										# all pad tokens have pos id = 2 and rest are between 2..seq_length
										# and the seq_length here is seq_length - num_pad_tokens
										# but when using past, there is no way of knowing if the past input ids had
										# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
										# position_ids being off by num_pad_tokens in past input
										_lowerCAmelCase							=							input_ids.clamp(self.pad_token_id + 1						)
										_lowerCAmelCase							=							decoder_input_ids.clamp(self.pad_token_id + 1						)

										_lowerCAmelCase							=							self.get_config()
										_lowerCAmelCase							=							config.num_attention_heads
										_lowerCAmelCase							=							self.prepare_inputs_dict(lowercase__  ,  lowercase__  ,  lowercase__						)
										return config, input_dict





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase			,		_lowerCAmelCase							=							self.prepare_config_and_inputs()
										return config, inputs_dict





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										return TaConfig(
										    vocab_size=1_66  ,  d_model=self.hidden_size  ,  d_ff=self.d_ff  ,  d_kv=self.hidden_size // self.num_attention_heads  ,  num_layers=self.num_hidden_layers  ,  num_decoder_layers=self.decoder_layers  ,  num_heads=self.num_attention_heads  ,  relative_attention_num_buckets=self.relative_attention_num_buckets  ,  dropout_rate=self.dropout_rate  ,  initializer_factor=self.initializer_factor  ,  eos_token_id=self.eos_token_id  ,  bos_token_id=self.pad_token_id  ,  pad_token_id=self.pad_token_id  ,  decoder_start_token_id=self.decoder_start_token_id  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										return TaConfig(
										    vocab_size=self.vocab_size  ,  d_model=self.hidden_size  ,  d_ff=self.d_ff  ,  d_kv=self.hidden_size // self.num_attention_heads  ,  num_layers=self.num_hidden_layers  ,  num_decoder_layers=self.decoder_layers  ,  num_heads=self.num_attention_heads  ,  relative_attention_num_buckets=self.relative_attention_num_buckets  ,  dropout_rate=self.dropout_rate  ,  initializer_factor=self.initializer_factor  ,  eos_token_id=self.eos_token_id  ,  bos_token_id=self.pad_token_id  ,  pad_token_id=self.pad_token_id  ,  decoder_start_token_id=self.decoder_start_token_id  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	List[str]  ,  lowercase__	:	str  ,  lowercase__	:	Any  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	Dict  ,  ):
										_lowerCAmelCase							=							UMTaModel(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(
										    input_ids=lowercase__  ,  decoder_input_ids=lowercase__  ,  attention_mask=lowercase__  ,  decoder_attention_mask=lowercase__  ,  )
										_lowerCAmelCase							=							model(input_ids=lowercase__  ,  decoder_input_ids=lowercase__						)
										_lowerCAmelCase							=							result.last_hidden_state
										_lowerCAmelCase							=							result.past_key_values
										_lowerCAmelCase							=							result.encoder_last_hidden_state

										self.parent.assertEqual(encoder_output.size()  ,  (self.batch_size, self.encoder_seq_length, self.hidden_size)						)
										self.parent.assertEqual(decoder_output.size()  ,  (self.batch_size, self.decoder_seq_length, self.hidden_size)						)
										# There should be `num_layers` key value embeddings stored in decoder_past
										self.parent.assertEqual(len(lowercase__						)  ,  config.num_layers						)
										# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
										self.parent.assertEqual(len(decoder_past[0]						)  ,  4						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	Tuple  ,  lowercase__	:	Tuple  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Dict  ,  lowercase__	:	str  ,  lowercase__	:	List[Any]  ,  ):
										_lowerCAmelCase							=							UMTaModel(config=lowercase__						).get_decoder().to(lowercase__						).eval()
										# first forward pass
										_lowerCAmelCase							=							model(lowercase__  ,  use_cache=lowercase__						)
										_lowerCAmelCase							=							model(lowercase__						)
										_lowerCAmelCase							=							model(lowercase__  ,  use_cache=lowercase__						)

										self.parent.assertTrue(len(lowercase__						) == len(lowercase__						)						)
										self.parent.assertTrue(len(lowercase__						) == len(lowercase__						) + 1						)

										_lowerCAmelCase			,		_lowerCAmelCase							=							outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										_lowerCAmelCase							=							ids_tensor((self.batch_size, 1)  ,  config.vocab_size						)

										# append to next input_ids and
										_lowerCAmelCase							=							torch.cat([input_ids, next_tokens]  ,  dim=-1						)

										_lowerCAmelCase							=							model(lowercase__						)['last_hidden_state']
										_lowerCAmelCase							=							model(lowercase__  ,  past_key_values=lowercase__						)['last_hidden_state']

										# select random slice
										_lowerCAmelCase							=							ids_tensor((1,)  ,  output_from_past.shape[-1]						).item()
										_lowerCAmelCase							=							output_from_no_past[:, -1, random_slice_idx].detach()
										_lowerCAmelCase							=							output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(lowercase__  ,  lowercase__  ,  atol=1e-3						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	Dict  ,  ):
										_lowerCAmelCase							=							UMTaModel(config=lowercase__						).to(lowercase__						).half().eval()
										_lowerCAmelCase							=							model(**lowercase__						)['last_hidden_state']
										self.parent.assertFalse(torch.isnan(lowercase__						).any().item()						)







@require_torch
class        lowerCamelCase__				(   UpperCAmelCase       ,UpperCAmelCase       ,UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=(
			    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
			)
			UpperCamelCase__    							=(UMTaForConditionalGeneration,) if is_torch_available() else ()
			UpperCamelCase__    							=(
			    {
			        "conversational": UMTaForConditionalGeneration,
			        "feature-extraction": UMTaModel,
			        "summarization": UMTaForConditionalGeneration,
			        "text2text-generation": UMTaForConditionalGeneration,
			        "translation": UMTaForConditionalGeneration,
			        "question-answering": UMTaForQuestionAnswering,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__    							=True
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False
			UpperCamelCase__    							=True
			UpperCamelCase__    							=True
			# The small UMT5 model needs higher percentages for CPU/MP tests
			UpperCamelCase__    							=[0.8, 0.9]





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							UMTaModelTester(self						)





			@unittest.skip('Test has a segmentation fault on torch 1.8.0'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										_lowerCAmelCase							=							UMTaModel(config_and_inputs[0]						).to(lowercase__						)
										with tempfile.TemporaryDirectory() as tmpdirname:
																	torch.onnx.export(
																	    lowercase__  ,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])  ,  f'{tmpdirname}/t5_test.onnx'  ,  export_params=lowercase__  ,  opset_version=9  ,  input_names=['input_ids', 'decoder_input_ids']  ,  )





			@unittest.skipIf(torch_device == 'cpu'  ,  'Cant do half precision'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model_fpaa_forward(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							['encoder_attentions', 'decoder_attentions', 'cross_attentions']
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										_lowerCAmelCase							=							config_and_inputs[0]
										_lowerCAmelCase							=							UMTaForConditionalGeneration(lowercase__						).eval()
										model.to(lowercase__						)

										_lowerCAmelCase							=							{
										    'head_mask': torch.zeros(config.num_layers  ,  config.num_heads  ,  device=lowercase__						),
										    'decoder_head_mask': torch.zeros(config.num_decoder_layers  ,  config.num_heads  ,  device=lowercase__						),
										    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers  ,  config.num_heads  ,  device=lowercase__						),
										}

										for attn_name, (name, mask) in zip(lowercase__  ,  head_masking.items()						):
																	_lowerCAmelCase							=							{name: mask}
																	# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																	if name == "head_mask":
																								_lowerCAmelCase							=							torch.ones(
																								    config.num_decoder_layers  ,  config.num_heads  ,  device=lowercase__						)

																	_lowerCAmelCase							=							model.generate(
																	    config_and_inputs[1]['input_ids']  ,  num_beams=1  ,  max_length=3  ,  output_attentions=lowercase__  ,  return_dict_in_generate=lowercase__  ,  **lowercase__  ,  )
																	# We check the state of decoder_attentions and cross_attentions just from the last step
																	_lowerCAmelCase							=							out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
																	self.assertEqual(sum([w.sum().item() for w in attn_weights]						)  ,  0.0						)





			@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										pass







@require_torch
@require_sentencepiece
@require_tokenizers
class        lowerCamelCase__				(   unittest.TestCase				):





			@slow
			@unittest.skip(
			    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							UMTaForConditionalGeneration.from_pretrained('google/umt5-small'  ,  return_dict=lowercase__						).to(lowercase__						)
										_lowerCAmelCase							=							AutoTokenizer.from_pretrained('google/umt5-small'  ,  use_fast=lowercase__  ,  legacy=lowercase__						)
										_lowerCAmelCase							=							[
										    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
										    'No se como puedo <extra_id_0>.',
										    'This is the reason why we <extra_id_0> them.',
										    'The <extra_id_0> walks in <extra_id_1>, seats',
										    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
										]
										_lowerCAmelCase							=							tokenizer(lowercase__  ,  return_tensors='pt'  ,  padding=lowercase__						).input_ids
										# fmt: off
										_lowerCAmelCase							=							torch.tensor(
										    [
										        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
										        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
										    ]						)
										# fmt: on
										torch.testing.assert_allclose(lowercase__  ,  lowercase__						)

										_lowerCAmelCase							=							model.generate(input_ids.to(lowercase__						)						)
										_lowerCAmelCase							=							[
										    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
										    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
										    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
										    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
										    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
										]
										_lowerCAmelCase							=							tokenizer.batch_decode(lowercase__						)
										self.assertEqual(lowercase__  ,  lowercase__						)






