






"""simple docstring"""



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						__A		:       List[str]  				=		'''platform'''

						import jax
						import jax.numpy as jnp

						from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    shift_tokens_right,
						)



def 						lowercase    (				__snake_case				: int		,					__snake_case				: str		,					__snake_case				: str=None		,					__snake_case				: int=None		,					__snake_case				: str=None		,					__snake_case				: Optional[Any]=None		,					__snake_case				: str=None		,					__snake_case				: str=None		,					):
		if attention_mask is None:
				lowercase_ : Dict							       =   np.where(input_ids != config.pad_token_id		,					1		,					0	)
		if decoder_attention_mask is None:
				lowercase_ : Optional[Any]							       =   np.where(decoder_input_ids != config.pad_token_id		,					1		,					0	)
		if head_mask is None:
				lowercase_ : str							       =   np.ones((config.encoder_layers, config.encoder_attention_heads)	)
		if decoder_head_mask is None:
				lowercase_ : Dict							       =   np.ones((config.decoder_layers, config.decoder_attention_heads)	)
		if cross_attn_head_mask is None:
				lowercase_ : int							       =   np.ones((config.decoder_layers, config.decoder_attention_heads)	)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}





class 					_UpperCAmelCase       :




					def __init__(				self     :						Dict    ,						A     :						Any    ,						A     :						List[str]=13    ,						A     :						List[Any]=7    ,						A     :						int=True    ,						A     :						Any=False    ,						A     :						int=99    ,						A     :						Tuple=16    ,						A     :						Optional[Any]=2    ,						A     :						Union[str, Any]=4    ,						A     :						Dict=4    ,						A     :						Optional[Any]="gelu"    ,						A     :						Optional[int]=0.1    ,						A     :						Tuple=0.1    ,						A     :						Optional[Any]=32    ,						A     :						Tuple=2    ,						A     :						Any=1    ,						A     :						List[str]=0    ,						A     :						Union[str, Any]=0.02    ,						)  ->  Dict:
							lowercase_ : Optional[int]							       =   parent
							lowercase_ : str							       =   batch_size
							lowercase_ : str							       =   seq_length
							lowercase_ : Tuple							       =   is_training
							lowercase_ : Optional[Any]							       =   use_labels
							lowercase_ : List[Any]							       =   vocab_size
							lowercase_ : str							       =   hidden_size
							lowercase_ : Dict							       =   num_hidden_layers
							lowercase_ : Any							       =   num_attention_heads
							lowercase_ : Dict							       =   intermediate_size
							lowercase_ : Tuple							       =   hidden_act
							lowercase_ : List[str]							       =   hidden_dropout_prob
							lowercase_ : Union[str, Any]							       =   attention_probs_dropout_prob
							lowercase_ : List[Any]							       =   max_position_embeddings
							lowercase_ : Optional[int]							       =   eos_token_id
							lowercase_ : List[Any]							       =   pad_token_id
							lowercase_ : Union[str, Any]							       =   bos_token_id
							lowercase_ : Optional[int]							       =   initializer_range




					def   A  (				self     :						Optional[Any]   )  ->  Tuple:
							lowercase_ : Any							       =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,						self.vocab_size   )    ,						3    ,						self.vocab_size   )
							lowercase_ : Tuple							       =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,						dtype=np.intaa   ))    ,						-1   )

							lowercase_ : Tuple							       =   shift_tokens_right(lowercase_    ,						1    ,						2   )

							lowercase_ : Union[str, Any]							       =   BlenderbotSmallConfig(
							    vocab_size=self.vocab_size    ,						d_model=self.hidden_size    ,						encoder_layers=self.num_hidden_layers    ,						decoder_layers=self.num_hidden_layers    ,						encoder_attention_heads=self.num_attention_heads    ,						decoder_attention_heads=self.num_attention_heads    ,						encoder_ffn_dim=self.intermediate_size    ,						decoder_ffn_dim=self.intermediate_size    ,						dropout=self.hidden_dropout_prob    ,						attention_dropout=self.attention_probs_dropout_prob    ,						max_position_embeddings=self.max_position_embeddings    ,						eos_token_id=self.eos_token_id    ,						bos_token_id=self.bos_token_id    ,						pad_token_id=self.pad_token_id    ,						initializer_range=self.initializer_range    ,						use_cache=lowercase_    ,						)
							lowercase_ : List[Any]							       =   prepare_blenderbot_inputs_dict(lowercase_    ,						lowercase_    ,						lowercase_   )
							return config, inputs_dict




					def   A  (				self     :						Tuple   )  ->  int:
							lowercase_ : List[Any]							       =   self.prepare_config_and_inputs()
							return config, inputs_dict




					def   A  (				self     :						Any    ,						A     :						List[Any]    ,						A     :						Any    ,						A     :						List[Any]   )  ->  List[Any]:
							lowercase_ : Union[str, Any]							       =   20
							lowercase_ : List[Any]							       =   model_class_name(lowercase_   )

							lowercase_ : Optional[int]							       =   model.encode(inputs_dict['''input_ids''']   )

							lowercase_ : Optional[int]							       =   (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							lowercase_ : Optional[int]							       =   model.init_cache(decoder_input_ids.shape[0]    ,						lowercase_    ,						lowercase_   )
							lowercase_ : Tuple							       =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,						dtype='''i4'''   )

							lowercase_ : List[str]							       =   jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]    ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,						)
							lowercase_ : int							       =   model.decode(
							    decoder_input_ids[:, :-1]    ,						lowercase_    ,						decoder_attention_mask=lowercase_    ,						past_key_values=lowercase_    ,						decoder_position_ids=lowercase_    ,						)

							lowercase_ : str							       =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,						dtype='''i4'''   )
							lowercase_ : Dict							       =   model.decode(
							    decoder_input_ids[:, -1:]    ,						lowercase_    ,						decoder_attention_mask=lowercase_    ,						past_key_values=outputs_cache.past_key_values    ,						decoder_position_ids=lowercase_    ,						)

							lowercase_ : Union[str, Any]							       =   model.decode(lowercase_    ,						lowercase_   )

							lowercase_ : Tuple							       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
							self.parent.assertTrue(diff < 1e-3    ,						msg=F'''Max diff is {diff}'''   )






					def   A  (				self     :						Any    ,						A     :						Union[str, Any]    ,						A     :						Dict    ,						A     :						Any   )  ->  Union[str, Any]:
							lowercase_ : Optional[Any]							       =   20
							lowercase_ : List[str]							       =   model_class_name(lowercase_   )

							lowercase_ : Any							       =   model.encode(inputs_dict['''input_ids''']   )

							lowercase_ : List[str]							       =   (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							lowercase_ : Tuple							       =   jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
							    ]    ,						axis=-1    ,						)

							lowercase_ : List[str]							       =   model.init_cache(decoder_input_ids.shape[0]    ,						lowercase_    ,						lowercase_   )
							lowercase_ : Tuple							       =   jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]    ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,						)

							lowercase_ : str							       =   model.decode(
							    decoder_input_ids[:, :-1]    ,						lowercase_    ,						decoder_attention_mask=lowercase_    ,						past_key_values=lowercase_    ,						decoder_position_ids=lowercase_    ,						)
							lowercase_ : Union[str, Any]							       =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,						dtype='''i4'''   )
							lowercase_ : int							       =   model.decode(
							    decoder_input_ids[:, -1:]    ,						lowercase_    ,						past_key_values=outputs_cache.past_key_values    ,						decoder_attention_mask=lowercase_    ,						decoder_position_ids=lowercase_    ,						)

							lowercase_ : int							       =   model.decode(lowercase_    ,						lowercase_    ,						decoder_attention_mask=lowercase_   )

							lowercase_ : Dict							       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
							self.parent.assertTrue(diff < 1e-3    ,						msg=F'''Max diff is {diff}'''   )





@require_flax
class 					_UpperCAmelCase       ( unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Dict							    =   99




					def   A  (				self     :						Any   )  ->  Tuple:
							lowercase_ : Tuple							       =   np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ]    ,						dtype=np.intaa    ,						)

							lowercase_ : Tuple							       =   input_ids.shape[0]
							lowercase_ : Union[str, Any]							       =   BlenderbotSmallConfig(
							    vocab_size=self.vocab_size    ,						d_model=24    ,						encoder_layers=2    ,						decoder_layers=2    ,						encoder_attention_heads=2    ,						decoder_attention_heads=2    ,						encoder_ffn_dim=32    ,						decoder_ffn_dim=32    ,						max_position_embeddings=48    ,						eos_token_id=2    ,						pad_token_id=1    ,						bos_token_id=0    ,						)
							return config, input_ids, batch_size




					def   A  (				self     :						str   )  ->  Tuple:
							lowercase_ : Optional[int]							       =   self._get_config_and_data()
							lowercase_ : Tuple							       =   FlaxBlenderbotSmallForConditionalGeneration(lowercase_   )
							lowercase_ : List[str]							       =   lm_model(input_ids=lowercase_   )
							lowercase_ : int							       =   (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs['''logits'''].shape    ,						lowercase_   )




					def   A  (				self     :						Optional[Any]   )  ->  str:
							lowercase_ : List[Any]							       =   BlenderbotSmallConfig(
							    vocab_size=self.vocab_size    ,						d_model=14    ,						encoder_layers=2    ,						decoder_layers=2    ,						encoder_attention_heads=2    ,						decoder_attention_heads=2    ,						encoder_ffn_dim=8    ,						decoder_ffn_dim=8    ,						max_position_embeddings=48    ,						)
							lowercase_ : Dict							       =   FlaxBlenderbotSmallForConditionalGeneration(lowercase_   )
							lowercase_ : Optional[int]							       =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,						dtype=np.intaa   )
							lowercase_ : Union[str, Any]							       =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,						dtype=np.intaa   )
							lowercase_ : Union[str, Any]							       =   lm_model(input_ids=lowercase_    ,						decoder_input_ids=lowercase_   )
							lowercase_ : Optional[int]							       =   (*summary.shape, config.vocab_size)
							self.assertEqual(outputs['''logits'''].shape    ,						lowercase_   )






					def   A  (				self     :						List[Any]   )  ->  str:
							lowercase_ : List[Any]							       =   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,						dtype=np.intaa   )
							lowercase_ : int							       =   shift_tokens_right(lowercase_    ,						1    ,						2   )
							lowercase_ : Optional[int]							       =   np.equal(lowercase_    ,						1   ).astype(np.floataa   ).sum()
							lowercase_ : Any							       =   np.equal(lowercase_    ,						1   ).astype(np.floataa   ).sum()
							self.assertEqual(shifted.shape    ,						input_ids.shape   )
							self.assertEqual(lowercase_    ,						n_pad_before - 1   )
							self.assertTrue(np.equal(shifted[:, 0]    ,						2   ).all()   )





@require_flax
class 					_UpperCAmelCase       ( __A   ,						unittest.TestCase   ,						__A							):
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   True
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   (
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE_		:				str							    =   (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




					def   A  (				self     :						Any   )  ->  List[str]:
							lowercase_ : Dict							       =   FlaxBlenderbotSmallModelTester(self   )




					def   A  (				self     :						str   )  ->  List[str]:
							lowercase_ : Any							       =   self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(lowercase_    ,						lowercase_    ,						lowercase_   )




					def   A  (				self     :						Any   )  ->  List[str]:
							lowercase_ : Dict							       =   self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_    ,						lowercase_    ,						lowercase_   )




					def   A  (				self     :						Tuple   )  ->  List[str]:
							lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__   ):
											lowercase_ : Optional[int]							       =   self._prepare_for_class(lowercase_    ,						lowercase_   )
											lowercase_ : Union[str, Any]							       =   model_class(lowercase_   )

											@jax.jit
											def encode_jitted(A     :						Any    ,						A     :						List[Any]=None    ,						**A     :						Dict   ):
													return model.encode(input_ids=lowercase_    ,						attention_mask=lowercase_   )

											with self.subTest('''JIT Enabled'''   ):
													lowercase_ : List[str]							       =   encode_jitted(**lowercase_   ).to_tuple()

											with self.subTest('''JIT Disabled'''   ):
													with jax.disable_jit():
															lowercase_ : Any							       =   encode_jitted(**lowercase_   ).to_tuple()

											self.assertEqual(len(lowercase_   )    ,						len(lowercase_   )   )
											for jitted_output, output in zip(lowercase_    ,						lowercase_   ):
													self.assertEqual(jitted_output.shape    ,						output.shape   )




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__   ):
											lowercase_ : int							       =   model_class(lowercase_   )
											lowercase_ : List[str]							       =   model.encode(inputs_dict['''input_ids''']    ,						inputs_dict['''attention_mask''']   )

											lowercase_ : int							       =   {
											    "decoder_input_ids": inputs_dict["decoder_input_ids"],
											    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
											    "encoder_outputs": encoder_outputs,
											}

											@jax.jit
											def decode_jitted(A     :						Any    ,						A     :						int    ,						A     :						Optional[int]   ):
													return model.decode(
													    decoder_input_ids=lowercase_    ,						decoder_attention_mask=lowercase_    ,						encoder_outputs=lowercase_    ,						)

											with self.subTest('''JIT Enabled'''   ):
													lowercase_ : Any							       =   decode_jitted(**lowercase_   ).to_tuple()

											with self.subTest('''JIT Disabled'''   ):
													with jax.disable_jit():
															lowercase_ : Tuple							       =   decode_jitted(**lowercase_   ).to_tuple()

											self.assertEqual(len(lowercase_   )    ,						len(lowercase_   )   )
											for jitted_output, output in zip(lowercase_    ,						lowercase_   ):
													self.assertEqual(jitted_output.shape    ,						output.shape   )






					@slow
					def   A  (				self     :						Dict   )  ->  int:
							for model_class_name in self.all_model_classes:
									lowercase_ : List[Any]							       =   model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''   )
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									lowercase_ : Optional[Any]							       =   np.ones((1, 1)   ) * model.config.eos_token_id
									lowercase_ : str							       =   model(lowercase_   )
									self.assertIsNotNone(lowercase_   )




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   snake_case_       (		__A  ,unittest.TestCase       ):
   __A	:		Union[str, Any]         =				LEDTokenizer
   __A	:		Union[str, Any]         =				LEDTokenizerFast
   __A	:		Optional[Any]         =				True






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Union[str, Any]:
       super().setUp()
       lowercase__ :   List[str]    					=				[
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       lowercase__ :   Optional[int]    					=				dict(zip(lowercase_	,						range(len(lowercase_  )  )  )  )
       lowercase__ :   str    					=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       lowercase__ :   Tuple    					=				{"unk_token": "<unk>"}

       lowercase__ :   Optional[Any]    					=				os.path.join(self.tmpdirname	,						VOCAB_FILES_NAMES["vocab_file"]  )
       lowercase__ :   Dict    					=				os.path.join(self.tmpdirname	,						VOCAB_FILES_NAMES["merges_file"]  )
       with open(self.vocab_file	,						"w"	,						encoding="utf-8"  ) as fp:
           fp.write(json.dumps(lowercase_  ) + "\n"  )
       with open(self.merges_file	,						"w"	,						encoding="utf-8"  ) as fp:
           fp.write("\n".join(lowercase_  )  )






   def       __UpperCamelCase     (      self				:     int	,						**lowercase_				:     str  )					->						List[Any]:
       kwargs.update(self.special_tokens_map  )
       return self.tokenizer_class.from_pretrained(self.tmpdirname	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     List[Any]	,						**lowercase_				:     Any  )					->						List[Any]:
       kwargs.update(self.special_tokens_map  )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     Any  )					->						Tuple:
       return "lower newer", "lower newer"






   @cached_property
   def       __UpperCamelCase     (      self				:     Tuple  )					->						Optional[Any]:
       return LEDTokenizer.from_pretrained("allenai/led-base-16384"  )






   @cached_property
   def       __UpperCamelCase     (      self				:     Tuple  )					->						int:
       return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"  )






   @require_torch
   def       __UpperCamelCase     (      self				:     int  )					->						List[Any]:
       lowercase__ :   Union[str, Any]    					=				["A long paragraph for summarization.", "Another paragraph for summarization."]
       lowercase__ :   str    					=				[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   Dict    					=				tokenizer(lowercase_	,						max_length=len(lowercase_  )	,						padding=lowercase_	,						return_tensors="pt"  )
           self.assertIsInstance(lowercase_	,						lowercase_  )

           self.assertEqual((2, 9)	,						batch.input_ids.shape  )
           self.assertEqual((2, 9)	,						batch.attention_mask.shape  )
           lowercase__ :   Union[str, Any]    					=				batch.input_ids.tolist()[0]
           self.assertListEqual(lowercase_	,						lowercase_  )






   @require_torch
   def       __UpperCamelCase     (      self				:     List[str]  )					->						Tuple:
       lowercase__ :   Dict    					=				["A long paragraph for summarization.", "Another paragraph for summarization."]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   Optional[int]    					=				tokenizer(lowercase_	,						padding=lowercase_	,						return_tensors="pt"  )
           self.assertIn("input_ids"	,						lowercase_  )
           self.assertIn("attention_mask"	,						lowercase_  )
           self.assertNotIn("labels"	,						lowercase_  )
           self.assertNotIn("decoder_attention_mask"	,						lowercase_  )






   @require_torch
   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Any:
       lowercase__ :   Dict    					=				[
           "Summary of the text.",
           "Another summary.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   Dict    					=				tokenizer(text_target=lowercase_	,						max_length=32	,						padding="max_length"	,						return_tensors="pt"  )
           self.assertEqual(32	,						targets["input_ids"].shape[1]  )






   @require_torch
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Tuple:
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   int    					=				tokenizer(
               ["I am a small frog" * 10_24, "I am a small frog"]	,						padding=lowercase_	,						truncation=lowercase_	,						return_tensors="pt"  )
           self.assertIsInstance(lowercase_	,						lowercase_  )
           self.assertEqual(batch.input_ids.shape	,						(2, 51_22)  )






   @require_torch
   def       __UpperCamelCase     (      self				:     List[str]  )					->						Any:
       lowercase__ :   Union[str, Any]    					=				["A long paragraph for summarization."]
       lowercase__ :   List[Any]    					=				[
           "Summary of the text.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   List[Any]    					=				tokenizer(lowercase_	,						return_tensors="pt"  )
           lowercase__ :   Dict    					=				tokenizer(text_target=lowercase_	,						return_tensors="pt"  )
           lowercase__ :   Optional[int]    					=				inputs["input_ids"]
           lowercase__ :   str    					=				targets["input_ids"]
           self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
           self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
           self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
           self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )






   @require_torch
   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Dict:
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   int    					=				["Summary of the text.", "Another summary."]
           lowercase__ :   List[str]    					=				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

           lowercase__ :   Tuple    					=				tokenizer(lowercase_	,						padding=lowercase_  )
           lowercase__ :   int    					=				[[0] * len(lowercase_  ) for x in encoded_output["input_ids"]]
           lowercase__ :   Any    					=				tokenizer.pad(lowercase_  )
           self.assertSequenceEqual(outputs["global_attention_mask"]	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     int  )					->						Union[str, Any]:
       pass







   def       __UpperCamelCase     (      self				:     int  )					->						Optional[Any]:
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
               lowercase__ :   List[Any]    					=				self.rust_tokenizer_class.from_pretrained(lowercase_	,						**lowercase_  )
               lowercase__ :   List[str]    					=				self.tokenizer_class.from_pretrained(lowercase_	,						**lowercase_  )
               lowercase__ :   List[Any]    					=				"A, <mask> AllenNLP sentence."
               lowercase__ :   Tuple    					=				tokenizer_r.encode_plus(lowercase_	,						add_special_tokens=lowercase_	,						return_token_type_ids=lowercase_  )
               lowercase__ :   List[str]    					=				tokenizer_p.encode_plus(lowercase_	,						add_special_tokens=lowercase_	,						return_token_type_ids=lowercase_  )
               self.assertEqual(sum(tokens_r["token_type_ids"]  )	,						sum(tokens_p["token_type_ids"]  )  )
               self.assertEqual(
                   sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  )	,						sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  )	,						)

               lowercase__ :   Any    					=				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
               lowercase__ :   Optional[Any]    					=				tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )
               self.assertSequenceEqual(tokens_p["input_ids"]	,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]  )
               self.assertSequenceEqual(tokens_r["input_ids"]	,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]  )

               self.assertSequenceEqual(
                   lowercase_	,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
               self.assertSequenceEqual(
                   lowercase_	,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )



