"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


SCREAMING_SNAKE_CASE__   =					get_tests_dir("fixtures/dummy_feature_extractor_config.json")
SCREAMING_SNAKE_CASE__   =					get_tests_dir("fixtures/vocab.json")
SCREAMING_SNAKE_CASE__   =					get_tests_dir("fixtures")



class 					lowerCAmelCase_      (     unittest.TestCase   ):



    """simple docstring"""
    _lowerCAmelCase       :      Optional[int]     					=  ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     0

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'      )
          self.assertIsInstance(lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
                snake_case        =     WavaVecaConfig()
                snake_case        =     AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'      )

                # save in new folder
                model_config.save_pretrained(lowerCAmelCase      )
                processor.save_pretrained(lowerCAmelCase      )

                snake_case        =     AutoProcessor.from_pretrained(lowerCAmelCase      )

          self.assertIsInstance(lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
                # copy relevant files
                copyfile(lowerCAmelCase						,      os.path.join(lowerCAmelCase						,      lowerCAmelCase      )      )
                copyfile(lowerCAmelCase						,      os.path.join(lowerCAmelCase						,      'vocab.json'      )      )

                snake_case        =     AutoProcessor.from_pretrained(lowerCAmelCase      )

          self.assertIsInstance(lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
                snake_case        =     WavaVecaFeatureExtractor()
                snake_case        =     AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'      )

                snake_case        =     WavaVecaProcessor(lowerCAmelCase						,      lowerCAmelCase      )

                # save in new folder
                processor.save_pretrained(lowerCAmelCase      )

                # drop `processor_class` in tokenizer
                with open(os.path.join(lowerCAmelCase						,      lowerCAmelCase      )						,      'r'      ) as f:
                      snake_case        =     json.load(lowerCAmelCase      )
                      config_dict.pop('processor_class'      )

                with open(os.path.join(lowerCAmelCase						,      lowerCAmelCase      )						,      'w'      ) as f:
                      f.write(json.dumps(lowerCAmelCase      )      )

                snake_case        =     AutoProcessor.from_pretrained(lowerCAmelCase      )

          self.assertIsInstance(lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
                snake_case        =     WavaVecaFeatureExtractor()
                snake_case        =     AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'      )

                snake_case        =     WavaVecaProcessor(lowerCAmelCase						,      lowerCAmelCase      )

                # save in new folder
                processor.save_pretrained(lowerCAmelCase      )

                # drop `processor_class` in feature extractor
                with open(os.path.join(lowerCAmelCase						,      lowerCAmelCase      )						,      'r'      ) as f:
                      snake_case        =     json.load(lowerCAmelCase      )
                      config_dict.pop('processor_class'      )

                with open(os.path.join(lowerCAmelCase						,      lowerCAmelCase      )						,      'w'      ) as f:
                      f.write(json.dumps(lowerCAmelCase      )      )

                snake_case        =     AutoProcessor.from_pretrained(lowerCAmelCase      )

          self.assertIsInstance(lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
                snake_case        =     WavaVecaConfig(processor_class='Wav2Vec2Processor'      )
                model_config.save_pretrained(lowerCAmelCase      )
                # copy relevant files
                copyfile(lowerCAmelCase						,      os.path.join(lowerCAmelCase						,      'vocab.json'      )      )
                # create emtpy sample processor
                with open(os.path.join(lowerCAmelCase						,      lowerCAmelCase      )						,      'w'      ) as f:
                      f.write('{}'      )

                snake_case        =     AutoProcessor.from_pretrained(lowerCAmelCase      )

          self.assertIsInstance(lowerCAmelCase						,      lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          with self.assertRaises(lowerCAmelCase      ):
                snake_case        =     AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'      )
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(lowerCAmelCase      ):
                snake_case        =     AutoProcessor.from_pretrained(
                    'hf-internal-testing/test_dynamic_processor'						,      trust_remote_code=lowerCAmelCase      )

          snake_case        =     AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'						,      trust_remote_code=lowerCAmelCase      )
          self.assertTrue(processor.special_attribute_present      )
          self.assertEqual(processor.__class__.__name__						,      'NewProcessor'      )

          snake_case        =     processor.feature_extractor
          self.assertTrue(feature_extractor.special_attribute_present      )
          self.assertEqual(feature_extractor.__class__.__name__						,      'NewFeatureExtractor'      )

          snake_case        =     processor.tokenizer
          self.assertTrue(tokenizer.special_attribute_present      )
          if is_tokenizers_available():
                self.assertEqual(tokenizer.__class__.__name__						,      'NewTokenizerFast'      )

                # Test we can also load the slow version
                snake_case        =     AutoProcessor.from_pretrained(
                    'hf-internal-testing/test_dynamic_processor'						,      trust_remote_code=lowerCAmelCase						,      use_fast=lowerCAmelCase      )
                snake_case        =     new_processor.tokenizer
                self.assertTrue(new_tokenizer.special_attribute_present      )
                self.assertEqual(new_tokenizer.__class__.__name__						,      'NewTokenizer'      )
          else:
                self.assertEqual(tokenizer.__class__.__name__						,      'NewTokenizer'      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          try:
                AutoConfig.register('custom'						,      lowerCAmelCase      )
                AutoFeatureExtractor.register(lowerCAmelCase						,      lowerCAmelCase      )
                AutoTokenizer.register(lowerCAmelCase						,      slow_tokenizer_class=lowerCAmelCase      )
                AutoProcessor.register(lowerCAmelCase						,      lowerCAmelCase      )
                # Trying to register something existing in the Transformers library will raise an error
                with self.assertRaises(lowerCAmelCase      ):
                      AutoProcessor.register(lowerCAmelCase						,      lowerCAmelCase      )

                # Now that the config is registered, it can be used as any other config with the auto-API
                snake_case        =     CustomFeatureExtractor.from_pretrained(lowerCAmelCase      )

                with tempfile.TemporaryDirectory() as tmp_dir:
                      snake_case        =     os.path.join(lowerCAmelCase						,      'vocab.txt'      )
                      with open(lowerCAmelCase						,      'w'						,      encoding='utf-8'      ) as vocab_writer:
                            vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]      )      )
                      snake_case        =     CustomTokenizer(lowerCAmelCase      )

                snake_case        =     CustomProcessor(lowerCAmelCase						,      lowerCAmelCase      )

                with tempfile.TemporaryDirectory() as tmp_dir:
                      processor.save_pretrained(lowerCAmelCase      )
                      snake_case        =     AutoProcessor.from_pretrained(lowerCAmelCase      )
                      self.assertIsInstance(lowerCAmelCase						,      lowerCAmelCase      )

          finally:
                if "custom" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["custom"]
                if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                      del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
                if CustomConfig in TOKENIZER_MAPPING._extra_content:
                      del TOKENIZER_MAPPING._extra_content[CustomConfig]
                if CustomConfig in PROCESSOR_MAPPING._extra_content:
                      del PROCESSOR_MAPPING._extra_content[CustomConfig]

    def 						snake_case	(			self      ):






          """simple docstring"""

          class 					lowerCAmelCase_      (     lowerCAmelCase   ):



              """simple docstring"""
              _lowerCAmelCase       :      List[Any]     					=  False





          class 					lowerCAmelCase_      (     lowerCAmelCase   ):



              """simple docstring"""
              _lowerCAmelCase       :      str     					=  False





          class 					lowerCAmelCase_      (     lowerCAmelCase   ):



              """simple docstring"""
              _lowerCAmelCase       :      Optional[Any]     					=  """AutoFeatureExtractor"""
              _lowerCAmelCase       :      Dict     					=  """AutoTokenizer"""
              _lowerCAmelCase       :      Optional[Any]     					=  False

          try:
                AutoConfig.register('custom'						,      lowerCAmelCase      )
                AutoFeatureExtractor.register(lowerCAmelCase						,      lowerCAmelCase      )
                AutoTokenizer.register(lowerCAmelCase						,      slow_tokenizer_class=lowerCAmelCase      )
                AutoProcessor.register(lowerCAmelCase						,      lowerCAmelCase      )
                # If remote code is not set, the default is to use local classes.
                snake_case        =     AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'      )
                self.assertEqual(processor.__class__.__name__						,      'NewProcessor'      )
                self.assertFalse(processor.special_attribute_present      )
                self.assertFalse(processor.feature_extractor.special_attribute_present      )
                self.assertFalse(processor.tokenizer.special_attribute_present      )

                # If remote code is disabled, we load the local ones.
                snake_case        =     AutoProcessor.from_pretrained(
                    'hf-internal-testing/test_dynamic_processor'						,      trust_remote_code=lowerCAmelCase      )
                self.assertEqual(processor.__class__.__name__						,      'NewProcessor'      )
                self.assertFalse(processor.special_attribute_present      )
                self.assertFalse(processor.feature_extractor.special_attribute_present      )
                self.assertFalse(processor.tokenizer.special_attribute_present      )

                # If remote is enabled, we load from the Hub.
                snake_case        =     AutoProcessor.from_pretrained(
                    'hf-internal-testing/test_dynamic_processor'						,      trust_remote_code=lowerCAmelCase      )
                self.assertEqual(processor.__class__.__name__						,      'NewProcessor'      )
                self.assertTrue(processor.special_attribute_present      )
                self.assertTrue(processor.feature_extractor.special_attribute_present      )
                self.assertTrue(processor.tokenizer.special_attribute_present      )

          finally:
                if "custom" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["custom"]
                if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                      del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
                if CustomConfig in TOKENIZER_MAPPING._extra_content:
                      del TOKENIZER_MAPPING._extra_content[CustomConfig]
                if CustomConfig in PROCESSOR_MAPPING._extra_content:
                      del PROCESSOR_MAPPING._extra_content[CustomConfig]

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert'      )
          self.assertEqual(processor.__class__.__name__						,      'BertTokenizerFast'      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext'      )
          self.assertEqual(processor.__class__.__name__						,      'ConvNextImageProcessor'      )





@is_staging_test
class 					lowerCAmelCase_      (     unittest.TestCase   ):



    """simple docstring"""
    _lowerCAmelCase       :      Optional[Any]     					=  ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]

    @classmethod
    def 						snake_case	(			cls      ):






          """simple docstring"""

          snake_case        =     TOKEN
          HfFolder.save_token(lowerCAmelCase      )

    @classmethod
    def 						snake_case	(			cls      ):






          """simple docstring"""

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

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

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

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     WavaVecaProcessor.from_pretrained(lowerCAmelCase      )
          with tempfile.TemporaryDirectory() as tmp_dir:
                processor.save_pretrained(
                    os.path.join(lowerCAmelCase						,      'test-processor'      )						,      push_to_hub=lowerCAmelCase						,      use_auth_token=self._token      )

                snake_case        =     WavaVecaProcessor.from_pretrained(F"""{USER}/test-processor"""      )
                for k, v in processor.feature_extractor.__dict__.items():
                      self.assertEqual(lowerCAmelCase						,      getattr(new_processor.feature_extractor						,      lowerCAmelCase      )      )
                self.assertDictEqual(new_processor.tokenizer.get_vocab()						,      processor.tokenizer.get_vocab()      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     WavaVecaProcessor.from_pretrained(lowerCAmelCase      )

          with tempfile.TemporaryDirectory() as tmp_dir:
                processor.save_pretrained(
                    os.path.join(lowerCAmelCase						,      'test-processor-org'      )						,      push_to_hub=lowerCAmelCase						,      use_auth_token=self._token						,      organization='valid_org'						,      )

                snake_case        =     WavaVecaProcessor.from_pretrained('valid_org/test-processor-org'      )
                for k, v in processor.feature_extractor.__dict__.items():
                      self.assertEqual(lowerCAmelCase						,      getattr(new_processor.feature_extractor						,      lowerCAmelCase      )      )
                self.assertDictEqual(new_processor.tokenizer.get_vocab()						,      processor.tokenizer.get_vocab()      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          CustomFeatureExtractor.register_for_auto_class()
          CustomTokenizer.register_for_auto_class()
          CustomProcessor.register_for_auto_class()

          snake_case        =     CustomFeatureExtractor.from_pretrained(lowerCAmelCase      )

          with tempfile.TemporaryDirectory() as tmp_dir:
                snake_case        =     os.path.join(lowerCAmelCase						,      'vocab.txt'      )
                with open(lowerCAmelCase						,      'w'						,      encoding='utf-8'      ) as vocab_writer:
                      vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]      )      )
                snake_case        =     CustomTokenizer(lowerCAmelCase      )

          snake_case        =     CustomProcessor(lowerCAmelCase						,      lowerCAmelCase      )

          with tempfile.TemporaryDirectory() as tmp_dir:
                create_repo(F"""{USER}/test-dynamic-processor"""						,      token=self._token      )
                snake_case        =     Repository(lowerCAmelCase						,      clone_from=F"""{USER}/test-dynamic-processor"""						,      token=self._token      )
                processor.save_pretrained(lowerCAmelCase      )

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

                # This has added the proper auto_map field to the tokenizer config
                with open(os.path.join(lowerCAmelCase						,      'tokenizer_config.json'      )      ) as f:
                      snake_case        =     json.load(lowerCAmelCase      )
                self.assertDictEqual(
                    tokenizer_config['auto_map']						,      {
                        'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
                        'AutoProcessor': 'custom_processing.CustomProcessor',
                    }						,      )

                # The code has been copied from fixtures
                self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase						,      'custom_feature_extraction.py'      )      )      )
                self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase						,      'custom_tokenization.py'      )      )      )
                self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase						,      'custom_processing.py'      )      )      )

                repo.push_to_hub()

          snake_case        =     AutoProcessor.from_pretrained(F"""{USER}/test-dynamic-processor"""						,      trust_remote_code=lowerCAmelCase      )
          # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
          self.assertEqual(new_processor.__class__.__name__						,      'CustomProcessor'      )



"""simple docstring"""
from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  [r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]

				@register_to_config
				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = 5_02_57						,      lowerCAmelCase = 10_24						,      lowerCAmelCase = 7_68						,      lowerCAmelCase = 12						,      lowerCAmelCase = 12						,      lowerCAmelCase = None						,      lowerCAmelCase = "gelu_new"						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 1E-5						,      lowerCAmelCase = 0.02						,      lowerCAmelCase = True						,      lowerCAmelCase = True						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      ):






										"""simple docstring"""

										super().__init__()

										snake_case        =     prefix_length

										if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
																raise ValueError(
																    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
																    F""" `n_embd`: {n_embd} are not equal."""      )

										snake_case        =     prefix_inner_dim
										snake_case        =     prefix_hidden_dim

										snake_case        =     (
										    nn.Linear(self.prefix_inner_dim						,      self.prefix_hidden_dim      )
										    if self.prefix_hidden_dim is not None
										    else nn.Identity()
										)
										snake_case        =     (
										    nn.Linear(self.prefix_hidden_dim						,      lowerCAmelCase      ) if self.prefix_hidden_dim is not None else nn.Identity()
										)

										snake_case        =     GPTaConfig(
										    vocab_size=lowerCAmelCase						,      n_positions=lowerCAmelCase						,      n_embd=lowerCAmelCase						,      n_layer=lowerCAmelCase						,      n_head=lowerCAmelCase						,      n_inner=lowerCAmelCase						,      activation_function=lowerCAmelCase						,      resid_pdrop=lowerCAmelCase						,      embd_pdrop=lowerCAmelCase						,      attn_pdrop=lowerCAmelCase						,      layer_norm_epsilon=lowerCAmelCase						,      initializer_range=lowerCAmelCase						,      scale_attn_weights=lowerCAmelCase						,      use_cache=lowerCAmelCase						,      scale_attn_by_inverse_layer_idx=lowerCAmelCase						,      reorder_and_upcast_attn=lowerCAmelCase						,      )
										snake_case        =     GPTaLMHeadModel(lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     self.transformer.transformer.wte(lowerCAmelCase      )
										snake_case        =     self.encode_prefix(lowerCAmelCase      )
										snake_case        =     self.decode_prefix(lowerCAmelCase      )
										snake_case        =     torch.cat((prefix_embeds, embedding_text)						,      dim=1      )

										if labels is not None:
																snake_case        =     self.get_dummy_token(input_ids.shape[0]						,      input_ids.device      )
																snake_case        =     torch.cat((dummy_token, input_ids)						,      dim=1      )
										snake_case        =     self.transformer(inputs_embeds=lowerCAmelCase						,      labels=lowerCAmelCase						,      attention_mask=lowerCAmelCase      )
										if self.prefix_hidden_dim is not None:
																return out, hidden
										else:
																return out

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										return torch.zeros(lowerCAmelCase						,      self.prefix_length						,      dtype=torch.intaa						,      device=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										return self.encode_prefix(lowerCAmelCase      )

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     torch.split(lowerCAmelCase						,      1						,      dim=0      )
										snake_case        =     []
										snake_case        =     []
										for feature in features:
																snake_case        =     self.decode_prefix(feature.to(lowerCAmelCase      )      )  # back to the clip feature
																# Only support beam search for now
																snake_case   ,snake_case        =     self.generate_beam(
																    input_embeds=lowerCAmelCase						,      device=lowerCAmelCase						,      eos_token_id=lowerCAmelCase      )
																generated_tokens.append(output_tokens[0]      )
																generated_seq_lengths.append(seq_lengths[0]      )
										snake_case        =     torch.stack(lowerCAmelCase      )
										snake_case        =     torch.stack(lowerCAmelCase      )
										return generated_tokens, generated_seq_lengths

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase = 5						,      lowerCAmelCase = 67						,      lowerCAmelCase = 1.0						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     eos_token_id
										snake_case        =     None
										snake_case        =     None
										snake_case        =     torch.ones(lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=torch.int      )
										snake_case        =     torch.zeros(lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=torch.bool      )

										if input_embeds is not None:
																snake_case        =     input_embeds
										else:
																snake_case        =     self.transformer.transformer.wte(lowerCAmelCase      )

										for i in range(lowerCAmelCase      ):
																snake_case        =     self.transformer(inputs_embeds=lowerCAmelCase      )
																snake_case        =     outputs.logits
																snake_case        =     logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																snake_case        =     logits.softmax(-1      ).log()

																if scores is None:
																						snake_case   ,snake_case        =     logits.topk(lowerCAmelCase						,      -1      )
																						snake_case        =     generated.expand(lowerCAmelCase						,      *generated.shape[1:]      )
																						snake_case   ,snake_case        =     next_tokens.permute(1						,      0      ), scores.squeeze(0      )
																						if tokens is None:
																												snake_case        =     next_tokens
																						else:
																												snake_case        =     tokens.expand(lowerCAmelCase						,      *tokens.shape[1:]      )
																												snake_case        =     torch.cat((tokens, next_tokens)						,      dim=1      )
																else:
																						snake_case        =     -float(np.inf      )
																						snake_case        =     0
																						snake_case        =     scores[:, None] + logits
																						seq_lengths[~is_stopped] += 1
																						snake_case        =     scores_sum / seq_lengths[:, None]
																						snake_case   ,snake_case        =     scores_sum_average.view(-1      ).topk(lowerCAmelCase						,      -1      )
																						snake_case        =     next_tokens // scores_sum.shape[1]
																						snake_case        =     seq_lengths[next_tokens_source]
																						snake_case        =     next_tokens % scores_sum.shape[1]
																						snake_case        =     next_tokens.unsqueeze(1      )
																						snake_case        =     tokens[next_tokens_source]
																						snake_case        =     torch.cat((tokens, next_tokens)						,      dim=1      )
																						snake_case        =     generated[next_tokens_source]
																						snake_case        =     scores_sum_average * seq_lengths
																						snake_case        =     is_stopped[next_tokens_source]

																snake_case        =     self.transformer.transformer.wte(next_tokens.squeeze()      ).view(generated.shape[0]						,      1						,      -1      )
																snake_case        =     torch.cat((generated, next_token_embed)						,      dim=1      )
																snake_case        =     is_stopped + next_tokens.eq(lowerCAmelCase      ).squeeze()
																if is_stopped.all():
																						break

										snake_case        =     scores / seq_lengths
										snake_case        =     scores.argsort(descending=lowerCAmelCase      )
										# tokens tensors are already padded to max_seq_length
										snake_case        =     [tokens[i] for i in order]
										snake_case        =     torch.stack(lowerCAmelCase						,      dim=0      )
										snake_case        =     torch.tensor([seq_lengths[i] for i in order]						,      dtype=seq_lengths.dtype      )
										return output_texts, seq_lengths


