'''simple docstring'''






import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase    :     List[str]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     Dict         =       {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

_UpperCAmelCase    :     str         =       {
    '''b0''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_24,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_40,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 14_08,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_60,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 15_36,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_00,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 17_92,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_80,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 20_48,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_56,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 23_04,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_28,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 25_60,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_00,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any				):
     _A    		=  EfficientNetConfig()
     _A    		=  CONFIG_MAP[model_name]['hidden_dim']
     _A    		=  CONFIG_MAP[model_name]['width_coef']
     _A    		=  CONFIG_MAP[model_name]['depth_coef']
     _A    		=  CONFIG_MAP[model_name]['image_size']
     _A    		=  CONFIG_MAP[model_name]['dropout_rate']
     _A    		=  CONFIG_MAP[model_name]['dw_padding']

     _A    		=  'huggingface/label-files'
     _A    		=  'imagenet-1k-id2label.json'
     _A    		=  1_0_0_0
     _A    		=  json.load(open(hf_hub_download(__snake_case   ,		__snake_case   ,		repo_type='dataset'				)   ,		'r'				)				)
     _A    		=  {int(__snake_case				): v for k, v in idalabel.items()}

     _A    		=  idalabel
     _A    		=  {v: k for k, v in idalabel.items()}
     return config





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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]				):
     _A    		=  CONFIG_MAP[model_name]['image_size']
     _A    		=  EfficientNetImageProcessor(
         size={'height': size, 'width': size}   ,		image_mean=[0.4_85, 0.4_56, 0.4_06]   ,		image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]   ,		do_center_crop=__snake_case   ,		)
     return preprocessor





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]				):
     _A    		=  [v.split('_'				)[0].split('block'				)[1] for v in original_param_names if v.startswith('block'				)]
     _A    		=  sorted(set(__snake_case				)				)
     _A    		=  len(__snake_case				)
     _A    		=  {b: str(__snake_case				) for b, i in zip(__snake_case   ,		range(__snake_case				)				)}

     _A    		=  []
     rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')				)
     rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')				)
     rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')				)
     rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')				)
     rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')				)

     for b in block_names:
          _A    		=  block_name_mapping[b]
          rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight')				)
          rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight')				)
          rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias')				)
          rename_keys.append(
              (F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')				)
          rename_keys.append(
              (F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')				)
          rename_keys.append(
              (F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')				)
          rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')				)
          rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')				)
          rename_keys.append(
              (F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')				)
          rename_keys.append(
              (F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')				)

          rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')				)
          rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')				)
          rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')				)
          rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')				)
          rename_keys.append(
              (F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight')				)
          rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight')				)
          rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias')				)
          rename_keys.append(
              (F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean')				)
          rename_keys.append(
              (F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var')				)

     rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')				)
     rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')				)
     rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')				)
     rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')				)
     rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')				)

     _A    		=  {}
     for item in rename_keys:
          if item[0] in original_param_names:
               _A    		=  'efficientnet.' + item[1]

     _A    		=  'classifier.weight'
     _A    		=  'classifier.bias'
     return key_mapping





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			List[Any]   ,		__snake_case							:			str				):
     for key, value in tf_params.items():
          if "normalization" in key:
               continue

          _A    		=  key_mapping[key]
          if "_conv" in key and "kernel" in key:
               _A    		=  torch.from_numpy(__snake_case				).permute(3   ,		2   ,		0   ,		1				)
          elif "depthwise_kernel" in key:
               _A    		=  torch.from_numpy(__snake_case				).permute(2   ,		3   ,		0   ,		1				)
          elif "kernel" in key:
               _A    		=  torch.from_numpy(np.transpose(__snake_case				)				)
          else:
               _A    		=  torch.from_numpy(__snake_case				)

          # Replace HF parameters with original TF model parameters
          assert hf_params[hf_key].shape == new_hf_value.shape
          hf_params[hf_key].copy_(__snake_case				)





@torch.no_grad()
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			Dict   ,		__snake_case							:			List[Any]   ,		__snake_case							:			Optional[int]				):
     _A    		=  model_classes[model_name](
         include_top=__snake_case   ,		weights='imagenet'   ,		input_tensor=__snake_case   ,		input_shape=__snake_case   ,		pooling=__snake_case   ,		classes=1_0_0_0   ,		classifier_activation='softmax'   ,		)

     _A    		=  original_model.trainable_variables
     _A    		=  original_model.non_trainable_variables
     _A    		=  {param.name: param.numpy() for param in tf_params}
     for param in tf_non_train_params:
          _A    		=  param.numpy()
     _A    		=  list(tf_params.keys()				)

     # Load HuggingFace model
     _A    		=  get_efficientnet_config(__snake_case				)
     _A    		=  EfficientNetForImageClassification(__snake_case				).eval()
     _A    		=  hf_model.state_dict()

     # Create src-to-dst parameter name mapping dictionary
     print('Converting parameters...'				)
     _A    		=  rename_keys(__snake_case				)
     replace_params(__snake_case   ,		__snake_case   ,		__snake_case				)

     # Initialize preprocessor and preprocess input image
     _A    		=  convert_image_processor(__snake_case				)
     _A    		=  preprocessor(images=prepare_img()   ,		return_tensors='pt'				)

     # HF model inference
     hf_model.eval()
     with torch.no_grad():
          _A    		=  hf_model(**__snake_case				)
     _A    		=  outputs.logits.detach().numpy()

     # Original model inference
     _A    		=  False
     _A    		=  CONFIG_MAP[model_name]['image_size']
     _A    		=  prepare_img().resize((image_size, image_size)   ,		resample=PIL.Image.NEAREST				)
     _A    		=  image.img_to_array(__snake_case				)
     _A    		=  np.expand_dims(__snake_case   ,		axis=0				)
     _A    		=  original_model.predict(__snake_case				)

     # Check whether original and HF model outputs match  -> np.allclose
     assert np.allclose(__snake_case   ,		__snake_case   ,		atol=1e-3				), "The predicted logits are not the same."
     print('Model outputs match!'				)

     if save_model:
          # Create folder to save model
          if not os.path.isdir(__snake_case				):
               os.mkdir(__snake_case				)
          # Save converted model and image processor
          hf_model.save_pretrained(__snake_case				)
          preprocessor.save_pretrained(__snake_case				)

     if push_to_hub:
          # Push model and image processor to hub
          print(F'Pushing converted {model_name} to the hub...'				)
          _A    		=  F'efficientnet-{model_name}'
          preprocessor.push_to_hub(__snake_case				)
          hf_model.push_to_hub(__snake_case				)


if __name__ == "__main__":
   _UpperCAmelCase    :     List[Any]         =       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--model_name''',
       default='''b0''',
       type=str,
       help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default='''hf_model''',
       type=str,
       help='''Path to the output PyTorch model directory.''',
   )
   parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
   parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

   _UpperCAmelCase    :     Optional[Any]         =       parser.parse_args()
   convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





"""simple docstring"""





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					:




			"""simple docstring"""







			def __init__(					self    :       Dict							, _snake_case    :       Any							, _snake_case    :       Union[str, Any]=99							, _snake_case    :       List[Any]=13							, _snake_case    :       Optional[Any]=7							, _snake_case    :       Union[str, Any]=9							, _snake_case    :       List[Any]=True							, _snake_case    :       Optional[int]=True							, _snake_case    :       Any=False							, _snake_case    :       str=32							, _snake_case    :       Any=5							, _snake_case    :       List[str]=4							, _snake_case    :       Dict=37							, _snake_case    :       List[Any]=8							, _snake_case    :       int=0.1							, _snake_case    :       List[str]=0.0_0_2							, _snake_case    :       Optional[Any]=1							, _snake_case    :       Union[str, Any]=0							, _snake_case    :       Any=0							, _snake_case    :       List[str]=None							, _snake_case    :       List[Any]=None							, )   ->			int:







								"""simple docstring"""
								A_								=    parent
								A_								=    batch_size
								A_								=    encoder_seq_length
								A_								=    decoder_seq_length
								# For common tests
								A_								=    self.decoder_seq_length
								A_								=    is_training
								A_								=    use_attention_mask
								A_								=    use_labels
								A_								=    vocab_size
								A_								=    hidden_size
								A_								=    num_hidden_layers
								A_								=    num_attention_heads
								A_								=    d_ff
								A_								=    relative_attention_num_buckets
								A_								=    dropout_rate
								A_								=    initializer_factor
								A_								=    eos_token_id
								A_								=    pad_token_id
								A_								=    decoder_start_token_id
								A_								=    None
								A_								=    decoder_layers




			def   lowerCamelCase__  (					self    :       Any						)   ->			Any:







								"""simple docstring"""
								return TaConfig.from_pretrained("google/umt5-base"						)




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       Optional[int]							, _snake_case    :       Optional[int]							, _snake_case    :       int							, _snake_case    :       Dict=None							, _snake_case    :       Dict=None							, _snake_case    :       str=None							, _snake_case    :       Any=None							, _snake_case    :       Union[str, Any]=None							, )   ->			List[Any]:







								"""simple docstring"""
								if attention_mask is None:
													A_								=    input_ids.ne(config.pad_token_id						)
								if decoder_attention_mask is None:
													A_								=    decoder_input_ids.ne(config.pad_token_id						)
								if head_mask is None:
													A_								=    torch.ones(config.num_hidden_layers							, config.num_attention_heads							, device=_snake_case						)
								if decoder_head_mask is None:
													A_								=    torch.ones(config.num_decoder_layers							, config.num_attention_heads							, device=_snake_case						)
								if cross_attn_head_mask is None:
													A_								=    torch.ones(
													    config.num_decoder_layers							, config.num_attention_heads							, device=_snake_case						)
								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   lowerCamelCase__  (					self    :       int						)   ->			Tuple:







								"""simple docstring"""
								A_								=    ids_tensor([self.batch_size, self.encoder_seq_length]							, self.vocab_size						)
								A_								=    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
								A_								=    input_ids.clamp(self.pad_token_id + 1						)
								A_								=    decoder_input_ids.clamp(self.pad_token_id + 1						)

								A_								=    self.get_config()
								A_								=    config.num_attention_heads
								A_								=    self.prepare_inputs_dict(_snake_case							, _snake_case							, _snake_case						)
								return config, input_dict




			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			int:







								"""simple docstring"""
								A_			,     A_								=    self.prepare_config_and_inputs()
								return config, inputs_dict




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[str]:







								"""simple docstring"""
								return TaConfig(
								    vocab_size=166							, 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   lowerCamelCase__  (					self    :       Tuple						)   ->			Optional[Any]:







								"""simple docstring"""
								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   lowerCamelCase__  (					self    :       Optional[int]							, _snake_case    :       List[str]							, _snake_case    :       Any							, _snake_case    :       List[Any]							, _snake_case    :       Optional[int]							, _snake_case    :       Optional[Any]							, _snake_case    :       Optional[Any]							, )   ->			List[Any]:







								"""simple docstring"""
								A_								=    UMTaModel(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(
								    input_ids=_snake_case							, decoder_input_ids=_snake_case							, attention_mask=_snake_case							, decoder_attention_mask=_snake_case							, )
								A_								=    model(input_ids=_snake_case							, decoder_input_ids=_snake_case						)
								A_								=    result.last_hidden_state
								A_								=    result.past_key_values
								A_								=    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(_snake_case						)							, 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   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       Optional[Any]							, _snake_case    :       List[str]							, _snake_case    :       List[str]							, _snake_case    :       str							, _snake_case    :       Optional[Any]							, _snake_case    :       int							, )   ->			List[str]:







								"""simple docstring"""
								A_								=    UMTaModel(config=_snake_case						).get_decoder().to(_snake_case						).eval()
								# first forward pass
								A_								=    model(_snake_case							, use_cache=_snake_case						)
								A_								=    model(_snake_case						)
								A_								=    model(_snake_case							, use_cache=_snake_case						)

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

								A_			,     A_								=    outputs.to_tuple()

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

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

								A_								=    model(_snake_case						)["last_hidden_state"]
								A_								=    model(_snake_case							, past_key_values=_snake_case						)["last_hidden_state"]

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

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






			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       Any							, _snake_case    :       str							, )   ->			List[Any]:







								"""simple docstring"""
								A_								=    UMTaModel(config=_snake_case						).to(_snake_case						).half().eval()
								A_								=    model(**_snake_case						)["last_hidden_state"]
								self.parent.assertFalse(torch.isnan(_snake_case						).any().item()						)





@require_torch
class __lowerCAmelCase					(       _lowercase  ,					_lowercase  ,					_lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







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




			def   lowerCamelCase__  (					self    :       str						)   ->			Tuple:







								"""simple docstring"""
								A_								=    UMTaModelTester(self						)




			@unittest.skip("Test has a segmentation fault on torch 1.8.0"						)
			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								A_								=    UMTaModel(config_and_inputs[0]						).to(_snake_case						)
								with tempfile.TemporaryDirectory() as tmpdirname:
													torch.onnx.export(
													    _snake_case							, (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])							, F'{tmpdirname}/t5_test.onnx'							, export_params=_snake_case							, opset_version=9							, input_names=["input_ids", "decoder_input_ids"]							, )




			@unittest.skipIf(torch_device == "cpu"							, "Cant do half precision"						)
			def   lowerCamelCase__  (					self    :       Tuple						)   ->			List[str]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*_snake_case						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			Dict:







								"""simple docstring"""
								A_								=    ["encoder_attentions", "decoder_attentions", "cross_attentions"]
								A_								=    self.model_tester.prepare_config_and_inputs()
								A_								=    config_and_inputs[0]
								A_								=    UMTaForConditionalGeneration(_snake_case						).eval()
								model.to(_snake_case						)

								A_								=    {
								    "head_mask": torch.zeros(config.num_layers							, config.num_heads							, device=_snake_case						),
								    "decoder_head_mask": torch.zeros(config.num_decoder_layers							, config.num_heads							, device=_snake_case						),
								    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers							, config.num_heads							, device=_snake_case						),
								}

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

													A_								=    model.generate(
													    config_and_inputs[1]["input_ids"]							, num_beams=1							, max_length=3							, output_attentions=_snake_case							, return_dict_in_generate=_snake_case							, **_snake_case							, )
													# We check the state of decoder_attentions and cross_attentions just from the last step
													A_								=    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   lowerCamelCase__  (					self    :       Optional[int]						)   ->			List[str]:







								"""simple docstring"""
								pass





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




			"""simple docstring"""







			@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   lowerCamelCase__  (					self    :       Dict						)   ->			int:







								"""simple docstring"""
								A_								=    UMTaForConditionalGeneration.from_pretrained("google/umt5-small"							, return_dict=_snake_case						).to(_snake_case						)
								A_								=    AutoTokenizer.from_pretrained("google/umt5-small"							, use_fast=_snake_case							, legacy=_snake_case						)
								A_								=    [
								    "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>.",
								]
								A_								=    tokenizer(_snake_case							, return_tensors="pt"							, padding=_snake_case						).input_ids
								# fmt: off
								A_								=    torch.tensor(
								    [
								        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
								        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
								    ]						)
								# fmt: on
								torch.testing.assert_allclose(_snake_case							, _snake_case						)

								A_								=    model.generate(input_ids.to(_snake_case						)						)
								A_								=    [
								    "<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>",
								]
								A_								=    tokenizer.batch_decode(_snake_case						)
								self.assertEqual(_snake_case							, _snake_case						)




