






"""simple docstring"""






import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, 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 (
           FalconForCausalLM,
           FalconForQuestionAnswering,
           FalconForSequenceClassification,
           FalconForTokenClassification,
           FalconModel,
       )




class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						Any		,			lowercase_  :						Dict		,			lowercase_  :						str=3		,			lowercase_  :						Dict=7		,			lowercase_  :						Any=True		,			lowercase_  :						List[Any]=True		,			lowercase_  :						Union[str, Any]=False		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						Union[str, Any]=99		,			lowercase_  :						Dict=32		,			lowercase_  :						Union[str, Any]=5		,			lowercase_  :						Optional[Any]=4		,			lowercase_  :						List[str]=37		,			lowercase_  :						int="gelu"		,			lowercase_  :						int=0.1		,			lowercase_  :						List[str]=0.1		,			lowercase_  :						Any=512		,			lowercase_  :						List[Any]=16		,			lowercase_  :						Union[str, Any]=2		,			lowercase_  :						Dict=0.02		,			lowercase_  :						Optional[int]=3		,			lowercase_  :						Dict=4		,			lowercase_  :						Optional[int]=None		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	parent
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	batch_size
          SCREAMING_SNAKE_CASE_   :						List[str]							=	seq_length
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	is_training
          SCREAMING_SNAKE_CASE_   :						Dict							=	use_input_mask
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	use_token_type_ids
          SCREAMING_SNAKE_CASE_   :						str							=	use_labels
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						str							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						int							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						Dict							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						str							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						str							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						Dict							=	type_vocab_size
          SCREAMING_SNAKE_CASE_   :						str							=	type_sequence_label_size
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						int							=	num_labels
          SCREAMING_SNAKE_CASE_   :						Dict							=	num_choices
          SCREAMING_SNAKE_CASE_   :						Tuple							=	scope
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	None
          if self.use_input_mask:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	random_attention_mask([self.batch_size, self.seq_length])

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	None

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	None
          SCREAMING_SNAKE_CASE_   :						Dict							=	None
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	None
          if self.use_labels:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	ids_tensor([self.batch_size]		,			self.type_sequence_label_size)
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	ids_tensor([self.batch_size, self.seq_length]		,			self.num_labels)
              SCREAMING_SNAKE_CASE_   :						str							=	ids_tensor([self.batch_size]		,			self.num_choices)

          SCREAMING_SNAKE_CASE_   :						Dict							=	self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          return FalconConfig(
              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=lowercase_		,			initializer_range=self.initializer_range		,			pad_token_id=1		,			new_decoder_architecture=lowercase_		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						List[Any]		,			lowercase_  :						int		,			lowercase_  :						List[str]		,			lowercase_  :						Any		,			lowercase_  :						Optional[Any]		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	FalconModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(lowercase_		,			attention_mask=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(lowercase_)
          self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size))
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						int		,			lowercase_  :						Dict		,			lowercase_  :						int		,			lowercase_  :						str		,			lowercase_  :						str		,			lowercase_  :						int		,			lowercase_  :						Tuple		,			lowercase_  :						Dict		,			lowercase_  :						int		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	True
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	FalconModel(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model(
              lowercase_		,			attention_mask=lowercase_		,			encoder_hidden_states=lowercase_		,			encoder_attention_mask=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(
              lowercase_		,			attention_mask=lowercase_		,			encoder_hidden_states=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(lowercase_		,			attention_mask=lowercase_)
          self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size))
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						Tuple		,			lowercase_  :						int		,			lowercase_  :						Optional[Any]		,			lowercase_  :						Dict		,			lowercase_  :						Dict		,			lowercase_  :						Dict		,			lowercase_  :						List[str]		,			lowercase_  :						List[str]		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	FalconForCausalLM(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						Any							=	model(lowercase_		,			attention_mask=lowercase_		,			labels=lowercase_)
          self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size))
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						Any		,			lowercase_  :						int		,			lowercase_  :						str		,			lowercase_  :						Tuple		,			lowercase_  :						List[str]		,			lowercase_  :						Optional[int]		,			lowercase_  :						Tuple		,			lowercase_  :						Optional[int]		,			lowercase_  :						str		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	True
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	True
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	FalconForCausalLM(config=lowercase_)
          model.to(lowercase_)
          model.eval()

          # first forward pass
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(
              lowercase_		,			attention_mask=lowercase_		,			encoder_hidden_states=lowercase_		,			encoder_attention_mask=lowercase_		,			use_cache=lowercase_		,			)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	outputs.past_key_values

          # create hypothetical multiple next token and extent to next_input_ids
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	ids_tensor((self.batch_size, 3)		,			config.vocab_size)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	ids_tensor((self.batch_size, 3)		,			vocab_size=2)

          # append to next input_ids and
          SCREAMING_SNAKE_CASE_   :						Dict							=	torch.cat([input_ids, next_tokens]		,			dim=-1)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.cat([input_mask, next_mask]		,			dim=-1)

          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(
              lowercase_		,			attention_mask=lowercase_		,			encoder_hidden_states=lowercase_		,			encoder_attention_mask=lowercase_		,			output_hidden_states=lowercase_		,			)['''hidden_states'''][0]
          SCREAMING_SNAKE_CASE_   :						Any							=	model(
              lowercase_		,			attention_mask=lowercase_		,			encoder_hidden_states=lowercase_		,			encoder_attention_mask=lowercase_		,			past_key_values=lowercase_		,			output_hidden_states=lowercase_		,			)['''hidden_states'''][0]

          # select random slice
          SCREAMING_SNAKE_CASE_   :						int							=	ids_tensor((1,)		,			output_from_past.shape[-1]).item()
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	output_from_no_past[:, -3:, random_slice_idx].detach()
          SCREAMING_SNAKE_CASE_   :						Dict							=	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(lowercase_		,			lowercase_		,			atol=1e-3))



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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	self.prepare_config_and_inputs()
          (
              SCREAMING_SNAKE_CASE_    
          )   :						Tuple							=	config_and_inputs
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict




@require_torch
class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						(
          (
              FalconModel,
              FalconForCausalLM,
              FalconForSequenceClassification,
              FalconForTokenClassification,
              FalconForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      __UpperCamelCase							=						(FalconForCausalLM,) if is_torch_available() else ()
      __UpperCamelCase							=						(
          {
              "feature-extraction": FalconModel,
              "text-classification": FalconForSequenceClassification,
              "text-generation": FalconForCausalLM,
              "question-answering": FalconForQuestionAnswering,
              "token-classification": FalconForTokenClassification,
              "zero-shot": FalconForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase							=						False
      __UpperCamelCase							=						False
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	FalconModelTester(self)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	ConfigTester(self		,			config_class=lowercase_		,			hidden_size=37)
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          self.config_tester.run_common_tests()
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.model_tester.prepare_config_and_inputs()
          for alibi in [True, False]:
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	alibi
              self.model_tester.create_and_check_model(lowercase_		,			*lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						Tuple							=	3
          SCREAMING_SNAKE_CASE_   :						Tuple							=	input_dict['''input_ids''']
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	input_ids.ne(1).to(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Dict							=	ids_tensor([self.model_tester.batch_size]		,			self.model_tester.type_sequence_label_size)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	FalconForSequenceClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(lowercase_		,			attention_mask=lowercase_		,			labels=lowercase_)
          self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels))
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	3
          SCREAMING_SNAKE_CASE_   :						int							=	'''single_label_classification'''
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	input_dict['''input_ids''']
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	input_ids.ne(1).to(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Dict							=	ids_tensor([self.model_tester.batch_size]		,			self.model_tester.type_sequence_label_size)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	FalconForSequenceClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	model(lowercase_		,			attention_mask=lowercase_		,			labels=lowercase_)
          self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels))
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	input_dict['''input_ids''']
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	FalconForCausalLM(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						str							=	model(lowercase_		,			use_cache=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	input_ids.shape[0]
          SCREAMING_SNAKE_CASE_   :						Dict							=	model._convert_to_rw_cache(result.past_key_values)
          SCREAMING_SNAKE_CASE_   :						int							=	model._convert_cache_to_standard_format(lowercase_		,			lowercase_)
          for layer in range(len(lowercase_)):
              for tensor_idx in range(2):
                  self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3)
                  self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4)
                  self.assertTrue(
                      torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]))
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_   :						int							=	3
          SCREAMING_SNAKE_CASE_   :						Tuple							=	'''multi_label_classification'''
          SCREAMING_SNAKE_CASE_   :						Tuple							=	input_dict['''input_ids''']
          SCREAMING_SNAKE_CASE_   :						List[str]							=	input_ids.ne(1).to(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	ids_tensor(
              [self.model_tester.batch_size, config.num_labels]		,			self.model_tester.type_sequence_label_size).to(torch.float)
          SCREAMING_SNAKE_CASE_   :						int							=	FalconForSequenceClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_   :						Tuple							=	model(lowercase_		,			attention_mask=lowercase_		,			labels=lowercase_)
          self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels))



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




          '''simple docstring'''


          for model_class in self.all_generative_model_classes:
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.model_tester.prepare_config_and_inputs_for_common()

              # If it doesn't support cache, pass the test
              if not hasattr(lowercase_		,			'''use_cache'''):
                  return

              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_class(lowercase_).to(lowercase_)
              if "use_cache" not in inputs:
                  SCREAMING_SNAKE_CASE_   :						Dict							=	True
              SCREAMING_SNAKE_CASE_   :						str							=	model(**lowercase_)

              # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
              if "past_key_values" not in outputs:
                  return

              SCREAMING_SNAKE_CASE_   :						Tuple							=	(
                  getattr(lowercase_		,			'''decoder_layers'''		,			lowercase_)
                  or getattr(lowercase_		,			'''num_decoder_layers'''		,			lowercase_)
                  or config.num_hidden_layers
              )
              SCREAMING_SNAKE_CASE_   :						str							=	getattr(lowercase_		,			'''num_kv_heads'''		,			config.num_attention_heads)
              SCREAMING_SNAKE_CASE_   :						Tuple							=	getattr(lowercase_		,			'''d_model'''		,			config.hidden_size)
              SCREAMING_SNAKE_CASE_   :						Dict							=	embed_dim // num_attention_heads

              SCREAMING_SNAKE_CASE_   :						int							=	outputs['''past_key_values''']
              self.assertEqual(len(lowercase_)		,			lowercase_)

              SCREAMING_SNAKE_CASE_   :						List[Any]							=	inputs['''input_ids'''].shape
              for i in range(lowercase_):
                  if config.new_decoder_architecture:
                      SCREAMING_SNAKE_CASE_   :						Dict							=	config.num_attention_heads
                  elif config.multi_query:
                      SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	1
                  self.assertEqual(len(past_kv[0])		,			2)  # K V for the decoder = 2
                  self.assertEqual(
                      past_kv[i][0].shape		,			(batch_size, num_attention_heads, seq_length, per_head_embed_dim))
                  self.assertEqual(
                      past_kv[i][1].shape		,			(batch_size, num_attention_heads, seq_length, per_head_embed_dim))




@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b''')
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b''')
          model.eval()
          model.to(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer('''My favorite food is'''		,			return_tensors='''pt''').to(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	(
              '''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
          )

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	model.generate(**lowercase_		,			do_sample=lowercase_		,			max_new_tokens=19)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer.batch_decode(lowercase_)[0]

          self.assertEqual(lowercase_		,			lowercase_)
      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	AutoTokenizer.from_pretrained(lowercase_)
              SCREAMING_SNAKE_CASE_   :						str							=	FalconForCausalLM.from_pretrained(lowercase_)
              model.eval()
              model.to(lowercase_)
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer('''My favorite food is'''		,			return_tensors='''pt''').to(lowercase_)

              # We just test that these run without errors - the models are randomly initialized
              # and so the actual text outputs will be garbage
              model.generate(**lowercase_		,			do_sample=lowercase_		,			max_new_tokens=4)
              model.generate(**lowercase_		,			do_sample=lowercase_		,			max_new_tokens=4)
              model.generate(**lowercase_		,			num_beams=2		,			max_new_tokens=4)



      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          with torch.no_grad():
              for repo in [
                  "Rocketknight1/falcon-rw-1b",
                  "Rocketknight1/tiny-random-falcon-7b",
                  "Rocketknight1/tiny-random-falcon-40b",
              ]:
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	AutoTokenizer.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	FalconForCausalLM.from_pretrained(lowercase_)
                  model.eval()
                  model.to(device=lowercase_)
                  SCREAMING_SNAKE_CASE_   :						int							=	tokenizer('''My favorite food is'''		,			return_tensors='''pt''').to(lowercase_)

                  # Test results are the same with and without cache
                  SCREAMING_SNAKE_CASE_   :						Any							=	model.generate(**lowercase_		,			do_sample=lowercase_		,			max_new_tokens=20		,			use_cache=lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Dict							=	model.generate(**lowercase_		,			do_sample=lowercase_		,			max_new_tokens=20		,			use_cache=lowercase_)
                  self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0)










"""simple docstring"""






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  UpperCAmelCase_     :				Tuple         =				"""pt"""
elif is_tf_available():
  UpperCAmelCase_     :				Optional[Any]         =				"""tf"""
else:
  UpperCAmelCase_     :				str         =				"""jax"""




class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						ByTaTokenizer
      __UpperCamelCase							=						False
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          super().setUp()
          SCREAMING_SNAKE_CASE_   :						Dict							=	ByTaTokenizer()
          tokenizer.save_pretrained(self.tmpdirname)
      @cached_property
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          return ByTaTokenizer.from_pretrained('''google/byt5-small''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			**lowercase_  :						Tuple):




          '''simple docstring'''


          return self.tokenizer_class.from_pretrained(self.tmpdirname		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Optional[Any]		,			lowercase_  :						List[Any]=False		,			lowercase_  :						Tuple=20		,			lowercase_  :						List[str]=5):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	[]
          for i in range(len(lowercase_)):
              try:
                  SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer.decode([i]		,			clean_up_tokenization_spaces=lowercase_)
              except UnicodeDecodeError:
                  pass
              toks.append((i, tok))

          SCREAMING_SNAKE_CASE_   :						Dict							=	list(filter(lambda lowercase_: re.match(r'''^[ a-zA-Z]+$'''		,			t[1])		,			lowercase_))
          SCREAMING_SNAKE_CASE_   :						Any							=	list(filter(lambda lowercase_: [t[0]] == tokenizer.encode(t[1]		,			add_special_tokens=lowercase_)		,			lowercase_))
          if max_length is not None and len(lowercase_) > max_length:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	toks[:max_length]
          if min_length is not None and len(lowercase_) < min_length and len(lowercase_) > 0:
              while len(lowercase_) < min_length:
                  SCREAMING_SNAKE_CASE_   :						int							=	toks + toks
        # toks_str = [t[1] for t in toks]
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[t[0] for t in toks]

          # Ensure consistency
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer.decode(lowercase_		,			clean_up_tokenization_spaces=lowercase_)
          if " " not in output_txt and len(lowercase_) > 1:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	(
                  tokenizer.decode([toks_ids[0]]		,			clean_up_tokenization_spaces=lowercase_)
                  + ''' '''
                  + tokenizer.decode(toks_ids[1:]		,			clean_up_tokenization_spaces=lowercase_)
              )
          if with_prefix_space:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	''' ''' + output_txt
          SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
          return output_txt, output_ids
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>'''])
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(['''hi''', '''I went to the gym''', ''''''])
          self.assertListEqual(batch_with_eos_added['''input_ids''']		,			batch_without_eos_added['''input_ids'''])
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Any							=	'''Unicode €.'''
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
          self.assertEqual(encoded['''input_ids''']		,			lowercase_)

          # decoding
          SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.decode(lowercase_)
          self.assertEqual(lowercase_		,			'''Unicode €.</s>''')

          SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer('''e è é ê ë''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
          self.assertEqual(encoded['''input_ids''']		,			lowercase_)
          # decoding
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.decode(lowercase_)
          self.assertEqual(lowercase_		,			'''e è é ê ë</s>''')

          # encode/decode, but with `encode` instead of `__call__`
          self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''))		,			'''e è é ê ë</s>''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          # fmt: off
          SCREAMING_SNAKE_CASE_   :						Dict							=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
          # fmt: on
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer(lowercase_		,			padding=lowercase_		,			return_tensors=lowercase_)
          self.assertIsInstance(lowercase_		,			lowercase_)

          if FRAMEWORK != "jax":
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	list(batch.input_ids.numpy()[0])
          else:
              SCREAMING_SNAKE_CASE_   :						int							=	list(batch.input_ids.tolist()[0])

          self.assertListEqual(lowercase_		,			lowercase_)

          self.assertEqual((2, 37)		,			batch.input_ids.shape)
          self.assertEqual((2, 37)		,			batch.attention_mask.shape)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(lowercase_		,			padding=lowercase_		,			return_tensors=lowercase_)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn('''input_ids'''		,			lowercase_)
          self.assertIn('''attention_mask'''		,			lowercase_)
          self.assertNotIn('''decoder_input_ids'''		,			lowercase_)
          self.assertNotIn('''decoder_attention_mask'''		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						List[str]							=	[
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer(
              text_target=lowercase_		,			max_length=32		,			padding='''max_length'''		,			truncation=lowercase_		,			return_tensors=lowercase_)
          self.assertEqual(32		,			targets['''input_ids'''].shape[1])
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	['''A long paragraph for summarization. </s>''']
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''Summary of the text. </s>''']
          # fmt: off
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
          SCREAMING_SNAKE_CASE_   :						Tuple							=	[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
          # fmt: on

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(lowercase_		,			text_target=lowercase_)

          self.assertEqual(lowercase_		,			batch['''input_ids'''][0])
          self.assertEqual(lowercase_		,			batch['''labels'''][0])
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  self.assertNotEqual(tokenizer.model_max_length		,			42)

        # Now let's start the test
          SCREAMING_SNAKE_CASE_   :						str							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  # Isolate this from the other tests because we save additional tokens/etc
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	''' He is very happy, UNwant\u00E9d,running'''
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  tokenizer.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer.__class__.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	after_tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  self.assertListEqual(lowercase_		,			lowercase_)

                  shutil.rmtree(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	self.get_tokenizers(model_max_length=42)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  # Isolate this from the other tests because we save additional tokens/etc
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_   :						Any							=	''' He is very happy, UNwant\u00E9d,running'''
                  tokenizer.add_tokens(['''bim''', '''bambam'''])
                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer.additional_special_tokens
                  additional_special_tokens.append('''new_additional_special_token''')
                  tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens})
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  tokenizer.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.__class__.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	after_tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  self.assertListEqual(lowercase_		,			lowercase_)
                  self.assertIn('''new_additional_special_token'''		,			after_tokenizer.additional_special_tokens)
                  self.assertEqual(after_tokenizer.model_max_length		,			42)

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.__class__.from_pretrained(lowercase_		,			model_max_length=43)
                  self.assertEqual(tokenizer.model_max_length		,			43)

                  shutil.rmtree(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	[]
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(lowercase_)

                  with open(os.path.join(lowercase_		,			'''special_tokens_map.json''')		,			encoding='''utf-8''') as json_file:
                      SCREAMING_SNAKE_CASE_   :						List[Any]							=	json.load(lowercase_)

                  with open(os.path.join(lowercase_		,			'''tokenizer_config.json''')		,			encoding='''utf-8''') as json_file:
                      SCREAMING_SNAKE_CASE_   :						Tuple							=	json.load(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	[F'<extra_id_{i}>' for i in range(125)]

                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]
                  SCREAMING_SNAKE_CASE_   :						int							=	added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]

                  with open(os.path.join(lowercase_		,			'''special_tokens_map.json''')		,			'''w'''		,			encoding='''utf-8''') as outfile:
                      json.dump(lowercase_		,			lowercase_)
                  with open(os.path.join(lowercase_		,			'''tokenizer_config.json''')		,			'''w'''		,			encoding='''utf-8''') as outfile:
                      json.dump(lowercase_		,			lowercase_)

                  # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                  # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                  # "special_tokens_map.json" files
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer_class.from_pretrained(
                      lowercase_		,			)
                  self.assertIn(
                      '''an_additional_special_token'''		,			tokenizer_without_change_in_init.additional_special_tokens)
                  # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                  self.assertEqual(
                      ['''an_additional_special_token''']		,			tokenizer_without_change_in_init.convert_ids_to_tokens(
                          tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']))		,			)

                  # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''		,			lstrip=lowercase_)]
                  SCREAMING_SNAKE_CASE_   :						int							=	tokenizer_class.from_pretrained(
                      lowercase_		,			additional_special_tokens=lowercase_		,			)

                  self.assertIn('''a_new_additional_special_token'''		,			tokenizer.additional_special_tokens)
                  self.assertEqual(
                      ['''a_new_additional_special_token''']		,			tokenizer.convert_ids_to_tokens(
                          tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']))		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	[]
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer_class.from_pretrained(lowercase_)

                  self.assertTrue(tokenizer.decode([255]) == '''''')
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.get_tokenizers(fast=lowercase_		,			do_lower_case=lowercase_)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_   :						Any							=	['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer.convert_tokens_to_string(lowercase_)

                  self.assertIsInstance(lowercase_		,			lowercase_)



      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_   :						int							=	[
                      '''bos_token''',
                      '''eos_token''',
                      '''unk_token''',
                      '''sep_token''',
                      '''pad_token''',
                      '''cls_token''',
                      '''mask_token''',
                  ]

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	0
                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer.convert_ids_to_tokens(
                      lowercase_		,			skip_special_tokens=lowercase_)

                  for attr in attributes_list:
                      setattr(lowercase_		,			attr + '''_id'''		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			lowercase_)		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			attr + '''_id''')		,			lowercase_)

                      setattr(lowercase_		,			attr + '''_id'''		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			lowercase_)		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			attr + '''_id''')		,			lowercase_)

                  setattr(lowercase_		,			'''additional_special_tokens_ids'''		,			[])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens''')		,			[])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens_ids''')		,			[])

                  setattr(lowercase_		,			'''additional_special_tokens_ids'''		,			[token_id_to_test_setters])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens''')		,			[token_to_test_setters])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens_ids''')		,			[token_id_to_test_setters])


