

import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


_UpperCAmelCase		:       str						    =	{
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 6_5536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 6_5536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 13_1072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
}





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							return torch.atana(UpperCamelCase__       ,							UpperCamelCase__				) / math.pi * 2





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   torch.sin(t * math.pi / 2				) ** 2
							snake_case_          =   (1 - sigma**2) ** 0.5
							return alpha_sigma_to_t(UpperCamelCase__       ,							UpperCamelCase__				)







class 			lowercase    (	lowercase_      ):
				pass







class 			lowercase    (	nn.Module      ):




				def __init__( self				,       snake_case	):
											super().__init__()

											snake_case_          =   DiffusionAttnUnetaD(snake_case				,       n_attn_layers=4	)
											snake_case_          =   deepcopy(self.diffusion	)
											snake_case_          =   torch.quasirandom.SobolEngine(1				,       scramble=snake_case	)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   MODELS_MAP[model_name]['url']
							os.system(F'''wget {url} ./'''				)

							return F'''./{model_name}.ckpt'''


_UpperCAmelCase		:       Optional[Any]						    =	{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
_UpperCAmelCase		:       int						    =	{
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
_UpperCAmelCase		:       Dict						    =	{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
    """8""": """resnets.3""",
    """9""": """attentions.3""",
    """10""": """resnets.4""",
    """11""": """attentions.4""",
    """12""": """resnets.5""",
    """13""": """attentions.5""",
}
_UpperCAmelCase		:       List[str]						    =	{
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

_UpperCAmelCase		:       str						    =	{
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

_UpperCAmelCase		:       Dict						    =	{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							if name.startswith('skip'				):
														return name.replace('skip'       ,							RES_CONV_MAP['skip']				)

							# name has to be of format main.{digit}
							if not name.startswith('main.'				):
														raise ValueError(F'''ResConvBlock error with {name}'''				)

							return name.replace(name[:6]       ,							RES_CONV_MAP[name[:6]]				)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							for key, value in ATTN_MAP.items():
														if name.startswith(UpperCamelCase__				) and not isinstance(UpperCamelCase__       ,							UpperCamelCase__				):
																					return name.replace(UpperCamelCase__       ,							UpperCamelCase__				)
														elif name.startswith(UpperCamelCase__				):
																					return [name.replace(UpperCamelCase__       ,							UpperCamelCase__				) for v in value]
							raise ValueError(F'''Attn error with {name}'''				)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__=13				):







							'''simple docstring'''






							snake_case_          =   input_string

							if string.split('.'				)[0] == "timestep_embed":
														return string.replace('timestep_embed'       ,							'time_proj'				)

							snake_case_          =   0
							if string.startswith('net.3.'				):
														depth += 1
														snake_case_          =   string[6:]
							elif string.startswith('net.'				):
														snake_case_          =   string[4:]

							while string.startswith('main.7.'				):
														depth += 1
														snake_case_          =   string[7:]

							if string.startswith('main.'				):
														snake_case_          =   string[5:]

							# mid block
							if string[:2].isdigit():
														snake_case_          =   string[:2]
														snake_case_          =   string[2:]
							else:
														snake_case_          =   string[0]
														snake_case_          =   string[1:]

							if depth == max_depth:
														snake_case_          =   MID_NUM_TO_LAYER[layer_num]
														snake_case_          =   'mid_block'
							elif depth > 0 and int(UpperCamelCase__				) < 7:
														snake_case_          =   DOWN_NUM_TO_LAYER[layer_num]
														snake_case_          =   F'''down_blocks.{depth}'''
							elif depth > 0 and int(UpperCamelCase__				) > 7:
														snake_case_          =   UP_NUM_TO_LAYER[layer_num]
														snake_case_          =   F'''up_blocks.{max_depth - depth - 1}'''
							elif depth == 0:
														snake_case_          =   DEPTH_0_TO_LAYER[layer_num]
														snake_case_          =   F'''up_blocks.{max_depth - 1}''' if int(UpperCamelCase__				) > 3 else 'down_blocks.0'

							if not string_left.startswith('.'				):
														raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.'''				)

							snake_case_          =   string_left[1:]

							if "resnets" in new_layer:
														snake_case_          =   convert_resconv_naming(UpperCamelCase__				)
							elif "attentions" in new_layer:
														snake_case_          =   convert_attn_naming(UpperCamelCase__				)
														snake_case_          =   new_string_left

							if not isinstance(UpperCamelCase__       ,							UpperCamelCase__				):
														snake_case_          =   prefix + '.' + new_layer + '.' + string_left
							else:
														snake_case_          =   [prefix + '.' + new_layer + '.' + s for s in string_left]
							return new_string





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   {}
							for k, v in state_dict.items():
														if k.endswith('kernel'				):
																					# up- and downsample layers, don't have trainable weights
																					continue

														snake_case_          =   rename(UpperCamelCase__				)

														# check if we need to transform from Conv => Linear for attention
														if isinstance(UpperCamelCase__       ,							UpperCamelCase__				):
																					snake_case_          =   transform_conv_attns(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														else:
																					snake_case_          =   v

							return new_state_dict





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if len(UpperCamelCase__				) == 1:
														if len(v.shape				) == 3:
																					# weight
																					snake_case_          =   v[:, :, 0]
														else:
																					# bias
																					snake_case_          =   v
							else:
														# qkv matrices
														snake_case_          =   v.shape[0]
														snake_case_          =   trippled_shape // 3
														for i in range(3				):
																					if len(v.shape				) == 3:
																												snake_case_          =   v[i * single_shape : (i + 1) * single_shape, :, 0]
																					else:
																												snake_case_          =   v[i * single_shape : (i + 1) * single_shape]
							return new_state_dict





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)

							snake_case_          =   args.model_path.split('/'				)[-1].split('.'				)[0]
							if not os.path.isfile(args.model_path				):
														assert (
														    model_name == args.model_path
														), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
														snake_case_          =   download(UpperCamelCase__				)

							snake_case_          =   MODELS_MAP[model_name]['sample_rate']
							snake_case_          =   MODELS_MAP[model_name]['sample_size']

							snake_case_          =   Object()
							snake_case_          =   sample_size
							snake_case_          =   sample_rate
							snake_case_          =   0

							snake_case_          =   UNetaDModel(sample_size=UpperCamelCase__       ,							sample_rate=UpperCamelCase__				)
							snake_case_          =   diffusers_model.state_dict()

							snake_case_          =   DiffusionUncond(UpperCamelCase__				)
							orig_model.load_state_dict(torch.load(args.model_path       ,							map_location=UpperCamelCase__				)['state_dict']				)
							snake_case_          =   orig_model.diffusion_ema.eval()
							snake_case_          =   orig_model.state_dict()
							snake_case_          =   rename_orig_weights(UpperCamelCase__				)

							snake_case_          =   set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
							snake_case_          =   set(diffusers_state_dict.keys()				) - set(renamed_state_dict.keys()				)

							assert len(UpperCamelCase__				) == 0, F'''Problem with {renamed_minus_diffusers}'''
							assert all(k.endswith('kernel'				) for k in list(UpperCamelCase__				)				), F'''Problem with {diffusers_minus_renamed}'''

							for key, value in renamed_state_dict.items():
														assert (
														    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
														), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
														if key == "time_proj.weight":
																					snake_case_          =   value.squeeze()

														snake_case_          =   value

							diffusers_model.load_state_dict(UpperCamelCase__				)

							snake_case_          =   100
							snake_case_          =   33

							snake_case_          =   IPNDMScheduler(num_train_timesteps=UpperCamelCase__				)

							snake_case_          =   torch.manual_seed(UpperCamelCase__				)
							snake_case_          =   torch.randn([1, 2, config.sample_size]       ,							generator=UpperCamelCase__				).to(UpperCamelCase__				)

							snake_case_          =   torch.linspace(1       ,							0       ,							steps + 1       ,							device=UpperCamelCase__				)[:-1]
							snake_case_          =   get_crash_schedule(UpperCamelCase__				)

							snake_case_          =   DanceDiffusionPipeline(unet=UpperCamelCase__       ,							scheduler=UpperCamelCase__				)

							snake_case_          =   torch.manual_seed(33				)
							snake_case_          =   pipe(num_inference_steps=UpperCamelCase__       ,							generator=UpperCamelCase__				).audios

							snake_case_          =   sampling.iplms_sample(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							{}				)
							snake_case_          =   generated.clamp(-1       ,							1				)

							snake_case_          =   (generated - audio).abs().sum()
							snake_case_          =   (generated - audio).abs().max()

							if args.save:
														pipe.save_pretrained(args.checkpoint_path				)

							print('Diff sum'       ,							UpperCamelCase__				)
							print('Diff max'       ,							UpperCamelCase__				)

							assert diff_max < 1E-3, F'''Diff max: {diff_max} is too much :-/'''

							print(F'''Conversion for {model_name} successful!'''				)


if __name__ == "__main__":
					_UpperCAmelCase		:       str						    =	argparse.ArgumentParser()

					parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
					parser.add_argument(
					    """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
					)
					parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
					_UpperCAmelCase		:       List[Any]						    =	parser.parse_args()

					main(args)







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 			lowercase    :




				def __init__( self				,       snake_case				,       snake_case=99				,       snake_case=13				,       snake_case=7				,       snake_case=9				,       snake_case=True				,       snake_case=True				,       snake_case=False				,       snake_case=32				,       snake_case=5				,       snake_case=4				,       snake_case=37				,       snake_case=8				,       snake_case=0.1				,       snake_case=0.0_02				,       snake_case=1				,       snake_case=0				,       snake_case=0				,       snake_case=None				,       snake_case=None				,       ):
											snake_case_          =   parent
											snake_case_          =   batch_size
											snake_case_          =   encoder_seq_length
											snake_case_          =   decoder_seq_length
											# For common tests
											snake_case_          =   self.decoder_seq_length
											snake_case_          =   is_training
											snake_case_          =   use_attention_mask
											snake_case_          =   use_labels
											snake_case_          =   vocab_size
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   d_ff
											snake_case_          =   relative_attention_num_buckets
											snake_case_          =   dropout_rate
											snake_case_          =   initializer_factor
											snake_case_          =   eos_token_id
											snake_case_          =   pad_token_id
											snake_case_          =   decoder_start_token_id
											snake_case_          =   None
											snake_case_          =   decoder_layers




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




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case=None				,       snake_case=None				,       snake_case=None				,       snake_case=None				,       snake_case=None				,       ):
											if attention_mask is None:
																		snake_case_          =   input_ids.ne(config.pad_token_id	)
											if decoder_attention_mask is None:
																		snake_case_          =   decoder_input_ids.ne(config.pad_token_id	)
											if head_mask is None:
																		snake_case_          =   torch.ones(config.num_hidden_layers				,       config.num_attention_heads				,       device=snake_case	)
											if decoder_head_mask is None:
																		snake_case_          =   torch.ones(config.num_decoder_layers				,       config.num_attention_heads				,       device=snake_case	)
											if cross_attn_head_mask is None:
																		snake_case_          =   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    a							( self	):
											snake_case_          =   ids_tensor([self.batch_size, self.encoder_seq_length]				,       self.vocab_size	)
											snake_case_          =   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
											snake_case_          =   input_ids.clamp(self.pad_token_id + 1	)
											snake_case_          =   decoder_input_ids.clamp(self.pad_token_id + 1	)

											snake_case_          =   self.get_config()
											snake_case_          =   config.num_attention_heads
											snake_case_          =   self.prepare_inputs_dict(snake_case				,       snake_case				,       snake_case	)
											return config, input_dict




				def    a							( self	):
											snake_case_    ,   snake_case_          =   self.prepare_config_and_inputs()
											return config, inputs_dict




				def    a							( self	):
											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    a							( self	):
											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    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   UMTaModel(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											snake_case_          =   model(
											    input_ids=snake_case				,       decoder_input_ids=snake_case				,       attention_mask=snake_case				,       decoder_attention_mask=snake_case				,       )
											snake_case_          =   model(input_ids=snake_case				,       decoder_input_ids=snake_case	)
											snake_case_          =   result.last_hidden_state
											snake_case_          =   result.past_key_values
											snake_case_          =   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    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   UMTaModel(config=snake_case	).get_decoder().to(snake_case	).eval()
											# first forward pass
											snake_case_          =   model(snake_case				,       use_cache=snake_case	)
											snake_case_          =   model(snake_case	)
											snake_case_          =   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	)

											snake_case_    ,   snake_case_          =   outputs.to_tuple()

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

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

											snake_case_          =   model(snake_case	)['last_hidden_state']
											snake_case_          =   model(snake_case				,       past_key_values=snake_case	)['last_hidden_state']

											# select random slice
											snake_case_          =   ids_tensor((1,)				,       output_from_past.shape[-1]	).item()
											snake_case_          =   output_from_no_past[:, -1, random_slice_idx].detach()
											snake_case_          =   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    a							( self				,       snake_case				,       snake_case				,       ):
											snake_case_          =   UMTaModel(config=snake_case	).to(snake_case	).half().eval()
											snake_case_          =   model(**snake_case	)['last_hidden_state']
											self.parent.assertFalse(torch.isnan(snake_case	).any().item()	)







@require_torch
class 			lowercase    (	lowercase_     ,  lowercase_     ,  lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      (
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				__SCREAMING_SNAKE_CASE :			int	      =      (UMTaForConditionalGeneration,) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      (
				    {
				        '''conversational''': UMTaForConditionalGeneration,
				        '''feature-extraction''': UMTaModel,
				        '''summarization''': UMTaForConditionalGeneration,
				        '''text2text-generation''': UMTaForConditionalGeneration,
				        '''translation''': UMTaForConditionalGeneration,
				        '''question-answering''': UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				__SCREAMING_SNAKE_CASE :			List[str]	      =      True
				__SCREAMING_SNAKE_CASE :			str	      =      False
				__SCREAMING_SNAKE_CASE :			int	      =      False
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      True
				__SCREAMING_SNAKE_CASE :			Any	      =      True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				__SCREAMING_SNAKE_CASE :			List[str]	      =      [0.8, 0.9]




				def    a							( self	):
											snake_case_          =   UMTaModelTester(self	)




				@unittest.skip('Test has a segmentation fault on torch 1.8.0'	)
				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											snake_case_          =   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    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model_fpaa_forward(*snake_case	)




				def    a							( self	):
											snake_case_          =   ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											snake_case_          =   config_and_inputs[0]
											snake_case_          =   UMTaForConditionalGeneration(snake_case	).eval()
											model.to(snake_case	)

											snake_case_          =   {
											    '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()	):
																		snake_case_          =   {name: mask}
																		# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																		if name == "head_mask":
																									snake_case_          =   torch.ones(
																									    config.num_decoder_layers				,       config.num_heads				,       device=snake_case	)

																		snake_case_          =   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
																		snake_case_          =   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    a							( self	):
											pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowercase    (	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    a							( self	):
											snake_case_          =   UMTaForConditionalGeneration.from_pretrained('google/umt5-small'				,       return_dict=snake_case	).to(snake_case	)
											snake_case_          =   AutoTokenizer.from_pretrained('google/umt5-small'				,       use_fast=snake_case				,       legacy=snake_case	)
											snake_case_          =   [
											    '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>.',
											]
											snake_case_          =   tokenizer(snake_case				,       return_tensors='pt'				,       padding=snake_case	).input_ids
											# fmt: off
											snake_case_          =   torch.tensor(
											    [
											        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
											        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
											    ]	)
											# fmt: on
											torch.testing.assert_allclose(snake_case				,       snake_case	)

											snake_case_          =   model.generate(input_ids.to(snake_case	)	)
											snake_case_          =   [
											    '<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>',
											]
											snake_case_          =   tokenizer.batch_decode(snake_case	)
											self.assertEqual(snake_case				,       snake_case	)




