
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 			A_  :




	'''simple docstring'''


	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=0.002						,       snake_case=1						,       snake_case=0						,       snake_case=0						,       snake_case=None						,       snake_case=None						,       ):
		lowercase	    =       parent
		lowercase	    =       batch_size
		lowercase	    =       encoder_seq_length
		lowercase	    =       decoder_seq_length
		# For common tests
		lowercase	    =       self.decoder_seq_length
		lowercase	    =       is_training
		lowercase	    =       use_attention_mask
		lowercase	    =       use_labels
		lowercase	    =       vocab_size
		lowercase	    =       hidden_size
		lowercase	    =       num_hidden_layers
		lowercase	    =       num_attention_heads
		lowercase	    =       d_ff
		lowercase	    =       relative_attention_num_buckets
		lowercase	    =       dropout_rate
		lowercase	    =       initializer_factor
		lowercase	    =       eos_token_id
		lowercase	    =       pad_token_id
		lowercase	    =       decoder_start_token_id
		lowercase	    =       None
		lowercase	    =       decoder_layers





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





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

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





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





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

		lowercase    ,   lowercase	    =       outputs.to_tuple()

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

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

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

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







@require_torch
class 			A_  (     __lowerCamelCase ,      __lowerCamelCase ,      __lowerCamelCase ,      unittest.TestCase  ):




	'''simple docstring'''


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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       UMTaModelTester(self			)





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





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

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

			lowercase	    =       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
			lowercase	    =       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			):
		pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 			A_  (     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       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       UMTaForConditionalGeneration.from_pretrained('google/umt5-small'						,       return_dict=snake_case			).to(snake_case			)
		lowercase	    =       AutoTokenizer.from_pretrained('google/umt5-small'						,       use_fast=snake_case						,       legacy=snake_case			)
		lowercase	    =       [
		    '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>.',
		]
		lowercase	    =       tokenizer(snake_case						,       return_tensors='pt'						,       padding=snake_case			).input_ids
		# fmt: off
		lowercase	    =       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			)

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






"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


A		=						logging.get_logger(__name__)

A		=						{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """bloom"""
			__magic_name__     					=     ["""past_key_values"""]
			__magic_name__     					=     {
			    """num_hidden_layers""": """n_layer""",
			    """num_attention_heads""": """n_head""",
			}



			def __init__(			self							:				Tuple   ,	_lowercase							:				List[Any]=250880   ,	_lowercase							:				int=64   ,	_lowercase							:				Optional[Any]=2   ,	_lowercase							:				Dict=8   ,	_lowercase							:				List[str]=1E-5   ,	_lowercase							:				List[Any]=0.02   ,	_lowercase							:				Optional[Any]=True   ,	_lowercase							:				Dict=1   ,	_lowercase							:				Union[str, Any]=2   ,	_lowercase							:				str=False   ,	_lowercase							:				List[Any]=0.0   ,	_lowercase							:				Tuple=0.0   ,	_lowercase							:				Dict=1   ,	_lowercase							:				int=False   ,	**_lowercase							:				int   ,	)							->					List[str]:
									snake_case   :			Any						    = vocab_size
									# Backward compatibility with n_embed kwarg
									snake_case   :			Any						    = kwargs.pop("n_embed"   ,	_lowercase		)
									snake_case   :			Tuple						    = hidden_size if n_embed is None else n_embed
									snake_case   :			Optional[Any]						    = n_layer
									snake_case   :			Optional[Any]						    = n_head
									snake_case   :			Union[str, Any]						    = layer_norm_epsilon
									snake_case   :			int						    = initializer_range
									snake_case   :			int						    = use_cache
									snake_case   :			int						    = pretraining_tp
									snake_case   :			Tuple						    = apply_residual_connection_post_layernorm
									snake_case   :			Union[str, Any]						    = hidden_dropout
									snake_case   :			Optional[Any]						    = attention_dropout

									snake_case   :			List[Any]						    = bos_token_id
									snake_case   :			Any						    = eos_token_id
									snake_case   :			Optional[Any]						    = slow_but_exact

									super().__init__(bos_token_id=_lowercase   ,	eos_token_id=_lowercase   ,	**_lowercase		)



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     version.parse("""1.12""")



			def __init__(			self							:				int   ,	_lowercase							:				PretrainedConfig   ,	_lowercase							:				str = "default"   ,	_lowercase							:				List[PatchingSpec] = None   ,	_lowercase							:				bool = False   ,	)							->					Dict:
									super().__init__(_lowercase   ,	task=_lowercase   ,	patching_specs=_lowercase   ,	use_past=_lowercase		)
									if not getattr(self._config   ,	"pad_token_id"   ,	_lowercase		):
															# TODO: how to do that better?
															snake_case   :			int						    = 0



			@property
			def 				__lowercase					(			self							:				str		)							->					Mapping[str, Mapping[int, str]]:
									snake_case   :			int						    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
									if self.use_past:
															# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
															self.fill_with_past_key_values_(_lowercase   ,	direction="inputs"   ,	inverted_values_shape=_lowercase		)
															snake_case   :			Optional[int]						    = {0: "batch", 1: "past_sequence + sequence"}
									else:
															snake_case   :			Dict						    = {0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def 				__lowercase					(			self							:				int		)							->					int:
									return self._config.n_layer



			@property
			def 				__lowercase					(			self							:				Dict		)							->					int:
									return self._config.n_head



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					float:
									return 1E-3



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				"PreTrainedTokenizer"   ,	_lowercase							:				int = -1   ,	_lowercase							:				int = -1   ,	_lowercase							:				bool = False   ,	_lowercase							:				Optional["TensorType"] = None   ,	)							->					Mapping[str, Any]:
									snake_case   :			int						    = super(_lowercase   ,	self		).generate_dummy_inputs(
									    _lowercase   ,	batch_size=_lowercase   ,	seq_length=_lowercase   ,	is_pair=_lowercase   ,	framework=_lowercase		)

									# We need to order the input in the way they appears in the forward()
									snake_case   :			Any						    = OrderedDict({"input_ids": common_inputs["input_ids"]}		)

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																					import torch

																					snake_case    ,			snake_case   :			List[str]						    = common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					snake_case   :			Optional[Any]						    = seqlen + 2
																					snake_case   :			Optional[int]						    = self._config.hidden_size // self.num_attention_heads
																					snake_case   :			List[str]						    = (
																					    batch * self.num_attention_heads,
																					    head_dim,
																					    past_key_values_length,
																					)
																					snake_case   :			Any						    = (
																					    batch * self.num_attention_heads,
																					    past_key_values_length,
																					    head_dim,
																					)
																					snake_case   :			Dict						    = [
																					    (torch.zeros(_lowercase		), torch.zeros(_lowercase		)) for _ in range(self.num_layers		)
																					]

									snake_case   :			Tuple						    = common_inputs["attention_mask"]
									if self.use_past:
															snake_case   :			Any						    = ordered_inputs["attention_mask"].dtype
															snake_case   :			List[str]						    = torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(_lowercase   ,	_lowercase   ,	dtype=_lowercase		)]   ,	dim=1		)

									return ordered_inputs



			@property
			def 				__lowercase					(			self							:				str		)							->					int:
									return 13



