

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 _UpperCamelCase				:
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							parent
				SCREAMING_SNAKE_CASE :   int					  =							batch_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							encoder_seq_length
				SCREAMING_SNAKE_CASE :   Tuple					  =							decoder_seq_length
				# For common tests
				SCREAMING_SNAKE_CASE :   int					  =							self.decoder_seq_length
				SCREAMING_SNAKE_CASE :   int					  =							is_training
				SCREAMING_SNAKE_CASE :   Any					  =							use_attention_mask
				SCREAMING_SNAKE_CASE :   List[str]					  =							use_labels
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Any					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							d_ff
				SCREAMING_SNAKE_CASE :   Tuple					  =							relative_attention_num_buckets
				SCREAMING_SNAKE_CASE :   List[Any]					  =							dropout_rate
				SCREAMING_SNAKE_CASE :   int					  =							initializer_factor
				SCREAMING_SNAKE_CASE :   List[Any]					  =							eos_token_id
				SCREAMING_SNAKE_CASE :   int					  =							pad_token_id
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							decoder_start_token_id
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							None
				SCREAMING_SNAKE_CASE :   Tuple					  =							decoder_layers
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Optional[Any]:

				"""simple docstring"""

				return TaConfig.from_pretrained("google/umt5-base"						)
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			List[str]   ,   a				:			Optional[Any]   ,   a				:			Optional[int]   ,   a				:			Tuple=None   ,   a				:			List[Any]=None   ,   a				:			int=None   ,   a				:			Any=None   ,   a				:			Dict=None   ,   )				->							List[str]:

				"""simple docstring"""

				if attention_mask is None:
					SCREAMING_SNAKE_CASE :   int					  =							input_ids.ne(config.pad_token_id						)
				if decoder_attention_mask is None:
					SCREAMING_SNAKE_CASE :   Any					  =							decoder_input_ids.ne(config.pad_token_id						)
				if head_mask is None:
					SCREAMING_SNAKE_CASE :   Tuple					  =							torch.ones(config.num_hidden_layers   ,   config.num_attention_heads   ,   device=a						)
				if decoder_head_mask is None:
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.ones(config.num_decoder_layers   ,   config.num_attention_heads   ,   device=a						)
				if cross_attn_head_mask is None:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							torch.ones(
					    config.num_decoder_layers   ,   config.num_attention_heads   ,   device=a						)
				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]						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							ids_tensor([self.batch_size, self.encoder_seq_length]   ,   self.vocab_size						)
				SCREAMING_SNAKE_CASE :   Any					  =							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
				SCREAMING_SNAKE_CASE :   int					  =							input_ids.clamp(self.pad_token_id + 1						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							decoder_input_ids.clamp(self.pad_token_id + 1						)

				SCREAMING_SNAKE_CASE :   Any					  =							self.get_config()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							config.num_attention_heads
				SCREAMING_SNAKE_CASE :   Dict					  =							self.prepare_inputs_dict(a   ,   a   ,   a						)
				return config, input_dict
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							self.prepare_config_and_inputs()
				return config, inputs_dict
			def 				__UpperCamelCase			(      self				:			int						)				->							List[Any]:

				"""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				:			List[Any]						)				->							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				:			Dict   ,   a				:			List[str]   ,   a				:			Dict   ,   a				:			int   ,   a				:			Tuple   ,   a				:			List[str]   ,   a				:			Union[str, Any]   ,   )				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							UMTaModel(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   List[str]					  =							model(
				    input_ids=a   ,   decoder_input_ids=a   ,   attention_mask=a   ,   decoder_attention_mask=a   ,   )
				SCREAMING_SNAKE_CASE :   Any					  =							model(input_ids=a   ,   decoder_input_ids=a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							result.last_hidden_state
				SCREAMING_SNAKE_CASE :   List[str]					  =							result.past_key_values
				SCREAMING_SNAKE_CASE :   List[str]					  =							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(a						)   ,   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				:			List[str]   ,   a				:			Optional[int]   ,   a				:			Tuple   ,   a				:			Tuple   ,   a				:			List[Any]   ,   a				:			List[Any]   ,   a				:			Optional[int]   ,   )				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							UMTaModel(config=a						).get_decoder().to(a						).eval()
				# first forward pass
				SCREAMING_SNAKE_CASE :   int					  =							model(a   ,   use_cache=a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							model(a						)
				SCREAMING_SNAKE_CASE :   str					  =							model(a   ,   use_cache=a						)

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

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							ids_tensor((self.batch_size, 1)   ,   config.vocab_size						)

				# append to next input_ids and
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.cat([input_ids, next_tokens]   ,   dim=-1						)

				SCREAMING_SNAKE_CASE :   str					  =							model(a						)["last_hidden_state"]
				SCREAMING_SNAKE_CASE :   List[str]					  =							model(a   ,   past_key_values=a						)["last_hidden_state"]

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

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






			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			Union[str, Any]   ,   a				:			int   ,   )				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							UMTaModel(config=a						).to(a						).half().eval()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(**a						)["last_hidden_state"]
				self.parent.assertFalse(torch.isnan(a						).any().item()						)






@require_torch
class _UpperCamelCase				(				__A				,						__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							UMTaModelTester(self						)
			@unittest.skip("Test has a segmentation fault on torch 1.8.0"						)
			def 				__UpperCamelCase			(      self				:			int						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.model_tester.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE :   int					  =							UMTaModel(config_and_inputs[0]						).to(a						)
				with tempfile.TemporaryDirectory() as tmpdirname:
					torch.onnx.export(
					    a   ,   (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])   ,   F"{tmpdirname}/t5_test.onnx"   ,   export_params=a   ,   opset_version=9   ,   input_names=["input_ids", "decoder_input_ids"]   ,   )
			@unittest.skipIf(torch_device == "cpu"   ,   "Cant do half precision"						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model_fpaa_forward(*a						)
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							["encoder_attentions", "decoder_attentions", "cross_attentions"]
				SCREAMING_SNAKE_CASE :   int					  =							self.model_tester.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							config_and_inputs[0]
				SCREAMING_SNAKE_CASE :   Any					  =							UMTaForConditionalGeneration(a						).eval()
				model.to(a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							{
				    "head_mask": torch.zeros(config.num_layers   ,   config.num_heads   ,   device=a						),
				    "decoder_head_mask": torch.zeros(config.num_decoder_layers   ,   config.num_heads   ,   device=a						),
				    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers   ,   config.num_heads   ,   device=a						),
				}

				for attn_name, (name, mask) in zip(a   ,   head_masking.items()						):
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							{name: mask}
					# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
					if name == "head_mask":
						SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.ones(
						    config.num_decoder_layers   ,   config.num_heads   ,   device=a						)

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

				"""simple docstring"""

				pass






@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCamelCase				(				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[str]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							UMTaForConditionalGeneration.from_pretrained("google/umt5-small"   ,   return_dict=a						).to(a						)
				SCREAMING_SNAKE_CASE :   int					  =							AutoTokenizer.from_pretrained("google/umt5-small"   ,   use_fast=a   ,   legacy=a						)
				SCREAMING_SNAKE_CASE :   int					  =							[
				    "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>.",
				]
				SCREAMING_SNAKE_CASE :   Dict					  =							tokenizer(a   ,   return_tensors="pt"   ,   padding=a						).input_ids
				# fmt: off
				SCREAMING_SNAKE_CASE :   int					  =							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(a   ,   a						)

				SCREAMING_SNAKE_CASE :   Dict					  =							model.generate(input_ids.to(a						)						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							[
				    "<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>",
				]
				SCREAMING_SNAKE_CASE :   Dict					  =							tokenizer.batch_decode(a						)
				self.assertEqual(a   ,   a						)


from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap

def 			lowerCamelCase__			(       _a):
	return getitem, k

def 			lowerCamelCase__			(       _a				,      _a):
	return setitem, k, v

def 			lowerCamelCase__			(       _a):
	return delitem, k

def 			lowerCamelCase__			(       _a				,      _a				,      *_a):
	try:
		return fun(_a				,      *_a), None
	except Exception as e:
		return None, e


a_      =     (
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
)

a_      =     [
    _set('key_a', 'val_a'),
    _set('key_a', 'val_b'),
]

a_      =     [
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
    _del('key_a'),
    _del('key_b'),
    _set('key_a', 'val_a'),
    _del('key_a'),
]

a_      =     [
    _get('key_a'),
    _del('key_a'),
    _set('key_a', 'val_a'),
    _del('key_a'),
    _del('key_a'),
    _get('key_a'),
]

a_      =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

a_      =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('key_a', 'val_b'),
]

@pytest.mark.parametrize(
    "operations"				,      (
        pytest.param(_add_items				,      id="add items"),
        pytest.param(_overwrite_items				,      id="overwrite items"),
        pytest.param(_delete_items				,      id="delete items"),
        pytest.param(_access_absent_items				,      id="access absent items"),
        pytest.param(_add_with_resize_up				,      id="add with resize up"),
        pytest.param(_add_with_resize_down				,      id="add with resize down"),
    )				,      )
def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							HashMap(initial_block_size=4)
	SCREAMING_SNAKE_CASE :   List[str]					  =							{}
	for _, (fun, *args) in enumerate(_a):
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							_run_operation(_a				,      _a				,      *_a)
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							_run_operation(_a				,      _a				,      *_a)
		assert my_res == py_res
		assert str(_a) == str(_a)
		assert set(_a) == set(_a)
		assert len(_a) == len(_a)
		assert set(my.items()) == set(py.items())


def 			lowerCamelCase__			(       ):
	def is_public(_a) -> bool:
		return not name.startswith("_")

	SCREAMING_SNAKE_CASE :   List[str]					  =							{name for name in dir({}) if is_public(_a)}
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							{name for name in dir(HashMap()) if is_public(_a)}

	assert dict_public_names > hash_public_names