





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 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     Any					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     Optional[Any]=99					,      __lowerCamelCase							:     Tuple=13					,      __lowerCamelCase							:     Dict=7					,      __lowerCamelCase							:     int=9					,      __lowerCamelCase							:     Dict=True					,      __lowerCamelCase							:     Any=True					,      __lowerCamelCase							:     Union[str, Any]=False					,      __lowerCamelCase							:     List[str]=32					,      __lowerCamelCase							:     str=5					,      __lowerCamelCase							:     List[Any]=4					,      __lowerCamelCase							:     str=37					,      __lowerCamelCase							:     Any=8					,      __lowerCamelCase							:     Dict=0.1					,      __lowerCamelCase							:     List[str]=0.002					,      __lowerCamelCase							:     Optional[Any]=1					,      __lowerCamelCase							:     int=0					,      __lowerCamelCase							:     Optional[int]=0					,      __lowerCamelCase							:     Any=None					,      __lowerCamelCase							:     List[str]=None					,      )       -> Union[str, Any]:

			'''simple docstring'''




			__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    UpperCAmelCase			(						self							:     Tuple   )       -> Dict:

			'''simple docstring'''




			return TaConfig.from_pretrained('google/umt5-base'   )




	def    UpperCAmelCase			(						self							:     Any					,      __lowerCamelCase							:     Dict					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     Optional[Any]					,      __lowerCamelCase							:     str=None					,      __lowerCamelCase							:     int=None					,      __lowerCamelCase							:     Optional[Any]=None					,      __lowerCamelCase							:     int=None					,      __lowerCamelCase							:     Optional[int]=None					,      )       -> Union[str, Any]:

			'''simple docstring'''




			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=__lowerCamelCase   )
			if decoder_head_mask is None:
					__lowercase											=     torch.ones(config.num_decoder_layers					,      config.num_attention_heads					,      device=__lowerCamelCase   )
			if cross_attn_head_mask is None:
					__lowercase											=     torch.ones(
					    config.num_decoder_layers					,      config.num_attention_heads					,      device=__lowerCamelCase   )
			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    UpperCAmelCase			(						self							:     List[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			__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(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase   )
			return config, input_dict




	def    UpperCAmelCase			(						self							:     Tuple   )       -> List[Any]:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     self.prepare_config_and_inputs()
			return config, inputs_dict




	def    UpperCAmelCase			(						self							:     int   )       -> Dict:

			'''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    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Optional[int]:

			'''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    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     Optional[int]					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Optional[Any]					,      __lowerCamelCase							:     List[Any]					,      __lowerCamelCase							:     str					,      )       -> Union[str, Any]:

			'''simple docstring'''




			__lowercase											=     UMTaModel(config=__lowerCamelCase   )
			model.to(__lowerCamelCase   )
			model.eval()
			__lowercase											=     model(
			    input_ids=__lowerCamelCase					,      decoder_input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      decoder_attention_mask=__lowerCamelCase					,      )
			__lowercase											=     model(input_ids=__lowerCamelCase					,      decoder_input_ids=__lowerCamelCase   )
			__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(__lowerCamelCase   )					,      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    UpperCAmelCase			(						self							:     Union[str, Any]					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     Union[str, Any]					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     Optional[Any]					,      )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     UMTaModel(config=__lowerCamelCase   ).get_decoder().to(__lowerCamelCase   ).eval()
			# first forward pass
			__lowercase											=     model(__lowerCamelCase					,      use_cache=__lowerCamelCase   )
			__lowercase											=     model(__lowerCamelCase   )
			__lowercase											=     model(__lowerCamelCase					,      use_cache=__lowerCamelCase   )

			self.parent.assertTrue(len(__lowerCamelCase   ) == len(__lowerCamelCase   )   )
			self.parent.assertTrue(len(__lowerCamelCase   ) == len(__lowerCamelCase   ) + 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(__lowerCamelCase   )['last_hidden_state']
			__lowercase											=     model(__lowerCamelCase					,      past_key_values=__lowerCamelCase   )['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(__lowerCamelCase					,      __lowerCamelCase					,      atol=1E-3   )   )






	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     Dict					,      __lowerCamelCase							:     List[str]					,      )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     UMTaModel(config=__lowerCamelCase   ).to(__lowerCamelCase   ).half().eval()
			__lowercase											=     model(**__lowerCamelCase   )['last_hidden_state']
			self.parent.assertFalse(torch.isnan(__lowerCamelCase   ).any().item()   )





@require_torch
class 		snake_case_   (							UpperCAmelCase_     ,     UpperCAmelCase_     ,     UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__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    UpperCAmelCase			(						self							:     int   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     UMTaModelTester(self   )




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

			'''simple docstring'''




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




	@unittest.skipIf(torch_device == 'cpu'					,      'Cant do half precision'   )
	def    UpperCAmelCase			(						self							:     Dict   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model_fpaa_forward(*__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Tuple   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
			__lowercase											=     self.model_tester.prepare_config_and_inputs()
			__lowercase											=     config_and_inputs[0]
			__lowercase											=     UMTaForConditionalGeneration(__lowerCamelCase   ).eval()
			model.to(__lowerCamelCase   )

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

			for attn_name, (name, mask) in zip(__lowerCamelCase					,      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=__lowerCamelCase   )

					__lowercase											=     model.generate(
					    config_and_inputs[1]['input_ids']					,      num_beams=1					,      max_length=3					,      output_attentions=__lowerCamelCase					,      return_dict_in_generate=__lowerCamelCase					,      **__lowerCamelCase					,      )
					# 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    UpperCAmelCase			(						self							:     List[str]   )       -> Union[str, Any]:

			'''simple docstring'''




			pass





@require_torch
@require_sentencepiece
@require_tokenizers
class 		snake_case_   (							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    UpperCAmelCase			(						self							:     List[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     UMTaForConditionalGeneration.from_pretrained('google/umt5-small'					,      return_dict=__lowerCamelCase   ).to(__lowerCamelCase   )
			__lowercase											=     AutoTokenizer.from_pretrained('google/umt5-small'					,      use_fast=__lowerCamelCase					,      legacy=__lowerCamelCase   )
			__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(__lowerCamelCase					,      return_tensors='pt'					,      padding=__lowerCamelCase   ).input_ids
			# fmt: off
			__lowercase											=     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(__lowerCamelCase					,      __lowerCamelCase   )

			__lowercase											=     model.generate(input_ids.to(__lowerCamelCase   )   )
			__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(__lowerCamelCase   )
			self.assertEqual(__lowerCamelCase					,      __lowerCamelCase   )











SCREAMING_SNAKE_CASE_   :      dict[str, float]			=   {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 100_0000,
    "gigajoule": 10_0000_0000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 360_0000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 418_6800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1055.0_5585,
    "footpound": 1.35_5818,
}



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    float:
		if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
				__lowercase											=     (
				    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
				    F"Valid values are: {', '.join(snake_case							)}"
				)
				raise ValueError(snake_case							)
		return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




