


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




									"""simple docstring"""





									_UpperCAmelCase											=     parent
									_UpperCAmelCase											=     batch_size
									_UpperCAmelCase											=     encoder_seq_length
									_UpperCAmelCase											=     decoder_seq_length
									# For common tests
									_UpperCAmelCase											=     self.decoder_seq_length
									_UpperCAmelCase											=     is_training
									_UpperCAmelCase											=     use_attention_mask
									_UpperCAmelCase											=     use_labels
									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     num_hidden_layers
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     d_ff
									_UpperCAmelCase											=     relative_attention_num_buckets
									_UpperCAmelCase											=     dropout_rate
									_UpperCAmelCase											=     initializer_factor
									_UpperCAmelCase											=     eos_token_id
									_UpperCAmelCase											=     pad_token_id
									_UpperCAmelCase											=     decoder_start_token_id
									_UpperCAmelCase											=     None
									_UpperCAmelCase											=     decoder_layers
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									return TaConfig.from_pretrained('google/umt5-base'		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       )					->			Any:




									"""simple docstring"""





									if attention_mask is None:
																_UpperCAmelCase											=     input_ids.ne(config.pad_token_id		)
									if decoder_attention_mask is None:
																_UpperCAmelCase											=     decoder_input_ids.ne(config.pad_token_id		)
									if head_mask is None:
																_UpperCAmelCase											=     torch.ones(config.num_hidden_layers     ,       config.num_attention_heads     ,       device=UpperCamelCase__		)
									if decoder_head_mask is None:
																_UpperCAmelCase											=     torch.ones(config.num_decoder_layers     ,       config.num_attention_heads     ,       device=UpperCamelCase__		)
									if cross_attn_head_mask is None:
																_UpperCAmelCase											=     torch.ones(
																    config.num_decoder_layers     ,       config.num_attention_heads     ,       device=UpperCamelCase__		)
									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		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     ids_tensor([self.batch_size, self.encoder_seq_length]     ,       self.vocab_size		)
									_UpperCAmelCase											=     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
									_UpperCAmelCase											=     input_ids.clamp(self.pad_token_id + 1		)
									_UpperCAmelCase											=     decoder_input_ids.clamp(self.pad_token_id + 1		)

									_UpperCAmelCase											=     self.get_config()
									_UpperCAmelCase											=     config.num_attention_heads
									_UpperCAmelCase											=     self.prepare_inputs_dict(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__		)
									return config, input_dict
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     self.prepare_config_and_inputs()
									return config, inputs_dict
		def       UpperCAmelCase__							(     self		)					->			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		)					->			Union[str, Any]:




									"""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     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       )					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     UMTaModel(config=UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									_UpperCAmelCase											=     model(
									    input_ids=UpperCamelCase__     ,       decoder_input_ids=UpperCamelCase__     ,       attention_mask=UpperCamelCase__     ,       decoder_attention_mask=UpperCamelCase__     ,       )
									_UpperCAmelCase											=     model(input_ids=UpperCamelCase__     ,       decoder_input_ids=UpperCamelCase__		)
									_UpperCAmelCase											=     result.last_hidden_state
									_UpperCAmelCase											=     result.past_key_values
									_UpperCAmelCase											=     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(UpperCamelCase__		)     ,       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     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       )					->			List[str]:




									"""simple docstring"""





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

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

									_UpperCAmelCase											=     outputs.to_tuple()

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

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

									_UpperCAmelCase											=     model(UpperCamelCase__		)["last_hidden_state"]
									_UpperCAmelCase											=     model(UpperCamelCase__     ,       past_key_values=UpperCamelCase__		)["last_hidden_state"]

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

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






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       )					->			str:




									"""simple docstring"""





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



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




									"""simple docstring"""





									_UpperCAmelCase											=     UMTaModelTester(self		)
		@unittest.skip('Test has a segmentation fault on torch 1.8.0'		)
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									_UpperCAmelCase											=     UMTaModel(config_and_inputs[0]		).to(UpperCamelCase__		)
									with tempfile.TemporaryDirectory() as tmpdirname:
																torch.onnx.export(
																    UpperCamelCase__     ,       (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])     ,       f'''{tmpdirname}/t5_test.onnx'''     ,       export_params=UpperCamelCase__     ,       opset_version=9     ,       input_names=['input_ids', 'decoder_input_ids']     ,       )
		@unittest.skipIf(torch_device == 'cpu'     ,       'Cant do half precision'		)
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase__		)
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     ["encoder_attentions", "decoder_attentions", "cross_attentions"]
									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									_UpperCAmelCase											=     config_and_inputs[0]
									_UpperCAmelCase											=     UMTaForConditionalGeneration(UpperCamelCase__		).eval()
									model.to(UpperCamelCase__		)

									_UpperCAmelCase											=     {
									    "head_mask": torch.zeros(config.num_layers     ,       config.num_heads     ,       device=UpperCamelCase__		),
									    "decoder_head_mask": torch.zeros(config.num_decoder_layers     ,       config.num_heads     ,       device=UpperCamelCase__		),
									    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers     ,       config.num_heads     ,       device=UpperCamelCase__		),
									}

									for attn_name, (name, mask) in zip(UpperCamelCase__     ,       head_masking.items()		):
																_UpperCAmelCase											=     {name: mask}
																# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																if name == "head_mask":
																							_UpperCAmelCase											=     torch.ones(
																							    config.num_decoder_layers     ,       config.num_heads     ,       device=UpperCamelCase__		)

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




									"""simple docstring"""





									pass



@require_torch
@require_sentencepiece
@require_tokenizers
class 						__a (							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       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     UMTaForConditionalGeneration.from_pretrained('google/umt5-small'     ,       return_dict=UpperCamelCase__		).to(UpperCamelCase__		)
									_UpperCAmelCase											=     AutoTokenizer.from_pretrained('google/umt5-small'     ,       use_fast=UpperCamelCase__     ,       legacy=UpperCamelCase__		)
									_UpperCAmelCase											=     [
									    "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>.",
									]
									_UpperCAmelCase											=     tokenizer(UpperCamelCase__     ,       return_tensors='pt'     ,       padding=UpperCamelCase__		).input_ids
									# fmt: off
									_UpperCAmelCase											=     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(UpperCamelCase__     ,       UpperCamelCase__		)

									_UpperCAmelCase											=     model.generate(input_ids.to(UpperCamelCase__		)		)
									_UpperCAmelCase											=     [
									    "<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>",
									]
									_UpperCAmelCase											=     tokenizer.batch_decode(UpperCamelCase__		)
									self.assertEqual(UpperCamelCase__     ,       UpperCamelCase__		)




"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    '''openai/whisper-base'''
   __UpperCAmelCase					:			Any				    =    (
       '''This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '''
       '''transcribed text.'''
   )
   __UpperCAmelCase					:			Tuple				    =    '''transcriber'''
   __UpperCAmelCase					:			Union[str, Any]				    =    WhisperProcessor
   __UpperCAmelCase					:			str				    =    WhisperForConditionalGeneration

   __UpperCAmelCase					:			int				    =    ['''audio''']
   __UpperCAmelCase					:			Optional[int]				    =    ['''text''']







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       return self.pre_processor(UpperCamelCase__		,      return_tensors="pt"		).input_features







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       return self.model.generate(inputs=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       return self.pre_processor.batch_decode(UpperCamelCase__		,      skip_special_tokens=UpperCamelCase__		)[0]





