

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

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

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


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

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
      from test_module.custom_tokenization_fast import CustomTokenizerFast




class      UpperCamelCase (     unittest.TestCase   ):

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			mock.Mock()
     SCREAMING_SNAKE_CASE								=			5_0_0
     SCREAMING_SNAKE_CASE								=			{}
     SCREAMING_SNAKE_CASE								=			HTTPError
     SCREAMING_SNAKE_CASE								=			{}

     # Download this model to make sure it's in the cache.
     SCREAMING_SNAKE_CASE								=			BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'    )

     # Under the mock environment we get a 500 error when trying to reach the tokenizer.
     with mock.patch('requests.Session.request'							,    return_value=snake_case__    ) as mock_head:
      SCREAMING_SNAKE_CASE								=			BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'    )
      # This check we did call the fake head request
      mock_head.assert_called()

    @require_tokenizers
    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			mock.Mock()
     SCREAMING_SNAKE_CASE								=			5_0_0
     SCREAMING_SNAKE_CASE								=			{}
     SCREAMING_SNAKE_CASE								=			HTTPError
     SCREAMING_SNAKE_CASE								=			{}

     # Download this model to make sure it's in the cache.
     SCREAMING_SNAKE_CASE								=			GPTaTokenizerFast.from_pretrained('gpt2'    )

     # Under the mock environment we get a 500 error when trying to reach the tokenizer.
     with mock.patch('requests.Session.request'							,    return_value=snake_case__    ) as mock_head:
      SCREAMING_SNAKE_CASE								=			GPTaTokenizerFast.from_pretrained('gpt2'    )
      # This check we did call the fake head request
      mock_head.assert_called()

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     try:
      SCREAMING_SNAKE_CASE								=			tempfile.mktemp()
      with open(snake_case__							,    'wb'    ) as f:
       http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'							,    snake_case__    )

      SCREAMING_SNAKE_CASE								=			AlbertTokenizer.from_pretrained(snake_case__    )
     finally:
      os.remove(snake_case__    )

     # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
     # the current folder and have the right name.
     if os.path.isfile('tokenizer.json'    ):
      # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
      return
     try:
      with open('tokenizer.json'							,    'wb'    ) as f:
       http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'							,    snake_case__    )
      SCREAMING_SNAKE_CASE								=			AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'    )
      # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
      self.assertEqual(tokenizer.vocab_size							,    1_0_0_0    )
      # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

     finally:
      os.remove('tokenizer.json'    )




    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'    )




@is_staging_test
class      UpperCamelCase (     unittest.TestCase   ):
    __UpperCamelCase         =["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]

    @classmethod
    def UpperCamelCase			(			cls :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TOKEN
     HfFolder.save_token(snake_case__    )

    @classmethod
    def UpperCamelCase			(			cls :      Optional[Any]    ):

     """simple docstring"""






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

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

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

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






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

     tokenizer.push_to_hub('test-tokenizer'							,    use_auth_token=self._token    )
     SCREAMING_SNAKE_CASE								=			BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer"""    )
     self.assertDictEqual(new_tokenizer.vocab							,    tokenizer.vocab    )

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

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

     SCREAMING_SNAKE_CASE								=			BertTokenizer.from_pretrained(F"""{USER}/test-tokenizer"""    )
     self.assertDictEqual(new_tokenizer.vocab							,    tokenizer.vocab    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






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

     tokenizer.push_to_hub('valid_org/test-tokenizer-org'							,    use_auth_token=self._token    )
     SCREAMING_SNAKE_CASE								=			BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'    )
     self.assertDictEqual(new_tokenizer.vocab							,    tokenizer.vocab    )

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

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

     SCREAMING_SNAKE_CASE								=			BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'    )
     self.assertDictEqual(new_tokenizer.vocab							,    tokenizer.vocab    )




    @require_tokenizers
    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






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

     # No fast custom tokenizer
     tokenizer.push_to_hub('test-dynamic-tokenizer'							,    use_auth_token=self._token    )

     SCREAMING_SNAKE_CASE								=			AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""							,    trust_remote_code=snake_case__    )
     # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
     self.assertEqual(tokenizer.__class__.__name__							,    'CustomTokenizer'    )

     # Fast and slow custom tokenizer
     CustomTokenizerFast.register_for_auto_class()
     with tempfile.TemporaryDirectory() as tmp_dir:
      SCREAMING_SNAKE_CASE								=			os.path.join(snake_case__							,    'vocab.txt'    )
      with open(snake_case__							,    'w'							,    encoding='utf-8'    ) as vocab_writer:
       vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]    )    )

      SCREAMING_SNAKE_CASE								=			BertTokenizerFast.from_pretrained(snake_case__    )
      bert_tokenizer.save_pretrained(snake_case__    )
      SCREAMING_SNAKE_CASE								=			CustomTokenizerFast.from_pretrained(snake_case__    )

     tokenizer.push_to_hub('test-dynamic-tokenizer'							,    use_auth_token=self._token    )

     SCREAMING_SNAKE_CASE								=			AutoTokenizer.from_pretrained(F"""{USER}/test-dynamic-tokenizer"""							,    trust_remote_code=snake_case__    )
     # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
     self.assertEqual(tokenizer.__class__.__name__							,    'CustomTokenizerFast'    )
     SCREAMING_SNAKE_CASE								=			AutoTokenizer.from_pretrained(
         F"""{USER}/test-dynamic-tokenizer"""							,    use_fast=snake_case__							,    trust_remote_code=snake_case__    )
     # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
     self.assertEqual(tokenizer.__class__.__name__							,    'CustomTokenizer'    )




class      UpperCamelCase (     unittest.TestCase   ):

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			Trie()
     trie.add('Hello 友達'    )
     self.assertEqual(trie.data							,    {'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}}    )
     trie.add('Hello'    )
     trie.data
     self.assertEqual(trie.data							,    {'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}}    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			Trie()
     self.assertEqual(trie.split('[CLS] This is a extra_id_100'    )							,    ['[CLS] This is a extra_id_100']    )
     trie.add('[CLS]'    )
     trie.add('extra_id_1'    )
     trie.add('extra_id_100'    )
     self.assertEqual(trie.split('[CLS] This is a extra_id_100'    )							,    ['[CLS]', ' This is a ', 'extra_id_100']    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			Trie()
     trie.add('A'    )
     self.assertEqual(trie.split('ABC'    )							,    ['A', 'BC']    )
     self.assertEqual(trie.split('BCA'    )							,    ['BC', 'A']    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			Trie()
     trie.add('TOKEN]'    )
     trie.add('[SPECIAL_TOKEN]'    )
     self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'    )							,    ['This is something ', '[SPECIAL_TOKEN]']    )

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			Trie()
     trie.add('A'    )
     trie.add('P'    )
     trie.add('[SPECIAL_TOKEN]'    )
     self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'    )							,    ['This is something ', '[SPECIAL_TOKEN]']    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			Trie()
     trie.add('AB'    )
     trie.add('B'    )
     trie.add('C'    )
     self.assertEqual(trie.split('ABC'    )							,    ['AB', 'C']    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			Trie()
     trie.add('ABC'    )
     trie.add('B'    )
     trie.add('CD'    )
     self.assertEqual(trie.split('ABCD'    )							,    ['ABC', 'D']    )




    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			Trie()
     SCREAMING_SNAKE_CASE								=			trie.cut_text('ABC'							,    [0, 0, 2, 1, 2, 3]    )
     self.assertEqual(snake_case__							,    ['AB', 'C']    )




import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

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


if is_torch_available():
      import torch

      from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel




class      UpperCamelCase :

    def __init__(			self :      Dict							,    snake_case__ :      str							,    snake_case__ :      str=1_3							,    snake_case__ :      Tuple=7							,    snake_case__ :      Tuple=True							,    snake_case__ :      Tuple=True							,    snake_case__ :      List[str]=False							,    snake_case__ :      Any=True							,    snake_case__ :      Union[str, Any]=9_9							,    snake_case__ :      Dict=3_2							,    snake_case__ :      Optional[Any]=5							,    snake_case__ :      Optional[Any]=4							,    snake_case__ :      Union[str, Any]=3_7							,    snake_case__ :      Tuple="gelu"							,    snake_case__ :      Dict=0.1							,    snake_case__ :      Any=0.1							,    snake_case__ :      int=5_1_2							,    snake_case__ :      Dict=1_6							,    snake_case__ :      str=2							,    snake_case__ :      Any=0.02							,    snake_case__ :      List[str]=3							,    snake_case__ :      int=4							,    snake_case__ :      List[str]=None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			parent
     SCREAMING_SNAKE_CASE								=			batch_size
     SCREAMING_SNAKE_CASE								=			seq_length
     SCREAMING_SNAKE_CASE								=			is_training
     SCREAMING_SNAKE_CASE								=			use_input_mask
     SCREAMING_SNAKE_CASE								=			use_token_type_ids
     SCREAMING_SNAKE_CASE								=			use_labels
     SCREAMING_SNAKE_CASE								=			vocab_size
     SCREAMING_SNAKE_CASE								=			hidden_size
     SCREAMING_SNAKE_CASE								=			num_hidden_layers
     SCREAMING_SNAKE_CASE								=			num_attention_heads
     SCREAMING_SNAKE_CASE								=			intermediate_size
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			hidden_dropout_prob
     SCREAMING_SNAKE_CASE								=			attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE								=			max_position_embeddings
     SCREAMING_SNAKE_CASE								=			type_vocab_size
     SCREAMING_SNAKE_CASE								=			type_sequence_label_size
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			num_labels
     SCREAMING_SNAKE_CASE								=			num_choices
     SCREAMING_SNAKE_CASE								=			scope

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_input_mask:
      SCREAMING_SNAKE_CASE								=			random_attention_mask([self.batch_size, self.seq_length]    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_token_type_ids:
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size    )

     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None
     if self.use_labels:
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size]							,    self.type_sequence_label_size    )
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.num_labels    )
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size]							,    self.num_choices    )

     SCREAMING_SNAKE_CASE								=			self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     return OpenLlamaConfig(
         vocab_size=self.vocab_size							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    hidden_act=self.hidden_act							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    max_position_embeddings=self.max_position_embeddings							,    type_vocab_size=self.type_vocab_size							,    is_decoder=snake_case__							,    initializer_range=self.initializer_range							,    use_stable_embedding=snake_case__							,    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      Tuple							,    snake_case__ :      str							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Any							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Dict							,    snake_case__ :      List[Any]							,    snake_case__ :      str							,    snake_case__ :      str							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      List[Any]							,    snake_case__ :      str							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Tuple							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			OpenLlamaForCausalLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)    )

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      Any							,    snake_case__ :      int							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      str							,    snake_case__ :      Union[str, Any]							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			OpenLlamaForCausalLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()

     # first forward pass
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    use_cache=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			outputs.past_key_values

     # create hypothetical multiple next token and extent to next_input_ids
     SCREAMING_SNAKE_CASE								=			ids_tensor((self.batch_size, 3)							,    config.vocab_size    )
     SCREAMING_SNAKE_CASE								=			ids_tensor((self.batch_size, 3)							,    vocab_size=2    )

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

     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    output_hidden_states=snake_case__							,    )['hidden_states'][0]
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    past_key_values=snake_case__							,    output_hidden_states=snake_case__							,    )['hidden_states'][0]

     # select random slice
     SCREAMING_SNAKE_CASE								=			ids_tensor((1,)							,    output_from_past.shape[-1]    ).item()
     SCREAMING_SNAKE_CASE								=			output_from_no_past[:, -3:, random_slice_idx].detach()
     SCREAMING_SNAKE_CASE								=			output_from_past[:, :, random_slice_idx].detach()

     self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

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




    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	
     )								=			config_and_inputs
     SCREAMING_SNAKE_CASE								=			{'input_ids': input_ids, 'attention_mask': input_mask}
     return config, inputs_dict




@require_torch
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(
        (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
    )
    __UpperCamelCase         =(OpenLlamaForCausalLM,) if is_torch_available() else ()
    __UpperCamelCase         =(
        {
            "feature-extraction": OpenLlamaModel,
            "text-classification": OpenLlamaForSequenceClassification,
            "text-generation": OpenLlamaForCausalLM,
            "zero-shot": OpenLlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase         =False
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			OpenLlamaModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
      SCREAMING_SNAKE_CASE								=			type
      self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor([self.model_tester.batch_size]							,    self.model_tester.type_sequence_label_size    )
     SCREAMING_SNAKE_CASE								=			OpenLlamaForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels)    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			'single_label_classification'
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor([self.model_tester.batch_size]							,    self.model_tester.type_sequence_label_size    )
     SCREAMING_SNAKE_CASE								=			OpenLlamaForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels)    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			'multi_label_classification'
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor(
         [self.model_tester.batch_size, config.num_labels]							,    self.model_tester.type_sequence_label_size    ).to(torch.float    )
     SCREAMING_SNAKE_CASE								=			OpenLlamaForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels)    )

    @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'    )
    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     pass




    @parameterized.expand([('linear',), ('dynamic',)]    )
    def UpperCamelCase			(			self :      str							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			ids_tensor([1, 1_0]							,    config.vocab_size    )
     SCREAMING_SNAKE_CASE								=			ids_tensor([1, int(config.max_position_embeddings * 1.5    )]							,    config.vocab_size    )

     set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     original_model.to(snake_case__    )
     original_model.eval()
     SCREAMING_SNAKE_CASE								=			original_model(snake_case__    ).last_hidden_state
     SCREAMING_SNAKE_CASE								=			original_model(snake_case__    ).last_hidden_state

     set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
     SCREAMING_SNAKE_CASE								=			{'type': scaling_type, 'factor': 10.0}
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     scaled_model.to(snake_case__    )
     scaled_model.eval()
     SCREAMING_SNAKE_CASE								=			scaled_model(snake_case__    ).last_hidden_state
     SCREAMING_SNAKE_CASE								=			scaled_model(snake_case__    ).last_hidden_state

     # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
     # maximum sequence length, so the outputs for the short input should match.
     if scaling_type == "dynamic":
      self.assertTrue(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-5    )    )
     else:
      self.assertFalse(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-5    )    )

     # The output should be different for long inputs
     self.assertFalse(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-5    )    )

