

'''simple docstring'''

import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel


class 	lowerCAmelCase  :







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







            """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       snake_case (				self      :			str   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			int    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Dict=None    ,   __lowercase      :			str=None    ,   __lowercase      :			Tuple=None    ,   __lowercase      :			int=None    ,   __lowercase      :			Optional[Any]=None    ,   ):







            """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=__lowercase   )
            if decoder_head_mask is None:
                 __lowercase      		=torch.ones(config.num_decoder_layers    ,   config.num_attention_heads    ,   device=__lowercase   )
            if cross_attn_head_mask is None:
                 __lowercase      		=torch.ones(
                     config.num_decoder_layers    ,   config.num_attention_heads    ,   device=__lowercase   )
            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       snake_case (				self      :			Tuple   ):







            """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(__lowercase    ,   __lowercase    ,   __lowercase   )
            return config, input_dict







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Tuple   ):







            """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       snake_case (				self      :			List[str]   ):







            """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       snake_case (				self      :			Any    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			Optional[int]    ,   ):







            """simple docstring"""







            __lowercase      		=UMTaModel(config=__lowercase   )
            model.to(__lowercase   )
            model.eval()
            __lowercase      		=model(
                input_ids=__lowercase    ,   decoder_input_ids=__lowercase    ,   attention_mask=__lowercase    ,   decoder_attention_mask=__lowercase    ,   )
            __lowercase      		=model(input_ids=__lowercase    ,   decoder_input_ids=__lowercase   )
            __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(__lowercase   )    ,   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       snake_case (				self      :			Tuple    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			List[str]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			int    ,   __lowercase      :			str    ,   ):







            """simple docstring"""







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

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







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			str    ,   __lowercase      :			List[Any]    ,   ):







            """simple docstring"""







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






@require_torch
class 	lowerCAmelCase  (   A ,      A ,      A ,      unittest.TestCase ):
       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       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=UMTaModelTester(self   )







       @unittest.skip('Test has a segmentation fault on torch 1.8.0'   )
       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







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







       @unittest.skipIf(torch_device == 'cpu'    ,   'Cant do half precision'   )
       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







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

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

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

                 __lowercase      		=model.generate(
                     config_and_inputs[1]['input_ids']    ,   num_beams=1    ,   max_length=3    ,   output_attentions=__lowercase    ,   return_dict_in_generate=__lowercase    ,   **__lowercase    ,   )
                 # 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       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCAmelCase  (   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       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		=UMTaForConditionalGeneration.from_pretrained('google/umt5-small'    ,   return_dict=__lowercase   ).to(__lowercase   )
            __lowercase      		=AutoTokenizer.from_pretrained('google/umt5-small'    ,   use_fast=__lowercase    ,   legacy=__lowercase   )
            __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(__lowercase    ,   return_tensors='pt'    ,   padding=__lowercase   ).input_ids
            # fmt: off
            __lowercase      		=torch.tensor(
                [
                    [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                    [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
                ]   )
            # fmt: on
            torch.testing.assert_allclose(__lowercase    ,   __lowercase   )

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





'''simple docstring'''

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase					 =	get_tests_dir('''fixtures''')


class 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=mock.Mock()
            __lowercase      		=500
            __lowercase      		={}
            __lowercase      		=HTTPError
            __lowercase      		={}

            # Download this model to make sure it's in the cache.
            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch('requests.Session.request'    ,   return_value=__lowercase   ) as mock_head:
                 __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'   )






@is_staging_test
class 	lowerCAmelCase  (   unittest.TestCase ):







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            __lowercase      		=TOKEN
            HfFolder.save_token(__lowercase   )







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            try:
                 delete_repo(token=cls._token    ,   repo_id='test-feature-extractor'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='valid_org/test-feature-extractor-org'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='test-dynamic-feature-extractor'   )
            except HTTPError:
                 pass







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='test-feature-extractor'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('valid_org/test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='valid_org/test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='valid_org/test-feature-extractor-org'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            CustomFeatureExtractor.register_for_auto_class()
            __lowercase      		=CustomFeatureExtractor.from_pretrained(__lowercase   )

            feature_extractor.push_to_hub('test-dynamic-feature-extractor'    ,   use_auth_token=self._token   )

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map    ,   {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}    ,   )

            __lowercase      		=AutoFeatureExtractor.from_pretrained(
                f'''{USER}/test-dynamic-feature-extractor'''    ,   trust_remote_code=__lowercase   )
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__    ,   'CustomFeatureExtractor'   )


