






"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase_							      = get_tests_dir('''fixtures/test_sentencepiece.model''')







@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							XGLMTokenizer
	__magic_name__       =							XGLMTokenizerFast
	__magic_name__       =							True
	__magic_name__       =							True





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Optional[Any]:
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCAmelCase_	:					Tuple     	=   XGLMTokenizer(lowerCAmelCase_      ,      keep_accents=lowerCAmelCase_		)
				tokenizer.save_pretrained(self.tmpdirname		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Optional[Any]:
				UpperCAmelCase_	:					Optional[int]     	=   "<pad>"
				UpperCAmelCase_	:					int     	=   1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_		)      ,      lowerCAmelCase_		)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_		)      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Dict     	=   list(self.get_tokenizer().get_vocab().keys()		)

				self.assertEqual(vocab_keys[0]      ,      "<s>"		)
				self.assertEqual(vocab_keys[1]      ,      "<pad>"		)
				self.assertEqual(len(lowerCAmelCase_		)      ,      1_008		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     List[Any]:
				self.assertEqual(self.get_tokenizer().vocab_size      ,      1_008		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     List[str]:
				UpperCAmelCase_	:					Tuple     	=   XGLMTokenizer(lowerCAmelCase_      ,      keep_accents=lowerCAmelCase_		)

				UpperCAmelCase_	:					Any     	=   tokenizer.tokenize("This is a test"		)
				self.assertListEqual(lowerCAmelCase_      ,      ["▁This", "▁is", "▁a", "▁t", "est"]		)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)      ,      [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,      )

				UpperCAmelCase_	:					List[Any]     	=   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
				self.assertListEqual(
				    lowerCAmelCase_      ,      [
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "9",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "é",
				        ".",
				    ]      ,      )
				UpperCAmelCase_	:					Optional[int]     	=   tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)
				self.assertListEqual(
				    lowerCAmelCase_      ,      [
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]      ,      )

				UpperCAmelCase_	:					str     	=   tokenizer.convert_ids_to_tokens(lowerCAmelCase_		)
				self.assertListEqual(
				    lowerCAmelCase_      ,      [
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "<unk>",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "<unk>",
				        ".",
				    ]      ,      )





	@cached_property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Tuple:
				return XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     int:
				with tempfile.NamedTemporaryFile() as f:
							shutil.copyfile(lowerCAmelCase_      ,      f.name		)
							UpperCAmelCase_	:					Optional[Any]     	=   XGLMTokenizer(f.name      ,      keep_accents=lowerCAmelCase_		)
							UpperCAmelCase_	:					Tuple     	=   pickle.dumps(lowerCAmelCase_		)
				pickle.loads(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Tuple:
				if not self.test_rust_tokenizer:
							return

				UpperCAmelCase_	:					List[str]     	=   self.get_tokenizer()
				UpperCAmelCase_	:					Tuple     	=   self.get_rust_tokenizer()

				UpperCAmelCase_	:					int     	=   "I was born in 92000, and this is falsé."

				UpperCAmelCase_	:					Optional[int]     	=   tokenizer.tokenize(lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   rust_tokenizer.tokenize(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Any     	=   tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   rust_tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   self.get_rust_tokenizer()
				UpperCAmelCase_	:					List[str]     	=   tokenizer.encode(lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   rust_tokenizer.encode(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				UpperCAmelCase_	:					Tuple     	=   "Hello World!"
				UpperCAmelCase_	:					Tuple     	=   [2, 31_227, 4_447, 35]

				self.assertListEqual(lowerCAmelCase_      ,      self.big_tokenizer.encode(lowerCAmelCase_		)		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Tuple:
				UpperCAmelCase_	:					List[str]     	=   (
				    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
				    " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
				)
				# fmt: off
				UpperCAmelCase_	:					Dict     	=   [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
				# fmt: on

				self.assertListEqual(lowerCAmelCase_      ,      self.big_tokenizer.encode(lowerCAmelCase_		)		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Any:
				# fmt: off
				UpperCAmelCase_	:					Union[str, Any]     	=   {
				    "input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
				    "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
				}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=lowerCAmelCase_      ,      model_name="facebook/xglm-564M"      ,      padding=lowerCAmelCase_      ,      )










from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    AutoTokenizer,
							    TFAutoModelForCausalLM,
							    TFAutoModelForSeqaSeqLM,
							    TFAutoModelForSpeechSeqaSeq,
							    TFAutoModelForVisionaSeq,
							    TFBartForConditionalGeneration,
							    TFLogitsProcessorList,
							    TFMinLengthLogitsProcessor,
							    tf_top_k_top_p_filtering,
							)

if is_tensorflow_text_available():
							import tensorflow_text as text
@require_tf
class     UpperCamelCase__					(     unittest.TestCase				):






		'''simple docstring'''






		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   str  				=     tf.convert_to_tensor(
									    [
									        [
									            8.2220991,  # 3rd highest value; idx. 0
									            -0.5620044,
									            5.23229752,
									            4.0386393,
									            -6.8798378,
									            -0.54785802,
									            -3.2012153,
									            2.92777176,
									            1.88171953,
									            7.35341276,  # 5th highest value; idx. 9
									            8.43207833,  # 2nd highest value; idx. 10
									            -9.85711836,
									            -5.96209236,
									            -1.13039161,
									            -7.1115294,
									            -0.8369633,
									            -5.3186408,
									            7.06427407,
									            0.81369344,
									            -0.82023817,
									            -5.9179796,
									            0.58813443,
									            -6.99778438,
									            4.71551189,
									            -0.18771637,
									            7.44020759,  # 4th highest value; idx. 25
									            9.38450987,  # 1st highest value; idx. 26
									            2.12662941,
									            -9.32562038,
									            2.35652522,
									        ],  # cummulative prob of 5 highest values <= 0.6
									        [
									            0.58425518,
									            4.53139238,
									            -5.57510464,
									            -6.28030699,
									            -7.19529503,
									            -4.02122551,
									            1.39337037,
									            -6.06707057,
									            1.59480517,
									            -9.643119,
									            0.03907799,
									            0.67231762,
									            -8.88206726,
									            6.27115922,  # 4th highest value; idx. 13
									            2.28520723,
									            4.82767506,
									            4.30421368,
									            8.8275313,  # 2nd highest value; idx. 17
									            5.44029958,  # 5th highest value; idx. 18
									            -4.4735794,
									            7.38579536,  # 3rd highest value; idx. 20
									            -2.91051663,
									            2.61946077,
									            -2.5674762,
									            -9.48959302,
									            -4.02922645,
									            -1.35416918,
									            9.67702323,  # 1st highest value; idx. 27
									            -5.89478553,
									            1.85370467,
									        ],  # cummulative prob of 5 highest values <= 0.6
									    ],   dtype=tf.floataa,   )

									lowercase_	:   int  				=     tf.convert_to_tensor(
									    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],   dtype=tf.intaa,   )  # expected non filtered idx as noted above

									lowercase_	:   List[Any]  				=     tf.convert_to_tensor(
									    [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],   dtype=tf.floataa,   )  # expected non filtered values as noted above

									lowercase_	:   int  				=     tf_top_k_top_p_filtering(snake_case__,   top_k=10,   top_p=0.6,   min_tokens_to_keep=4  )

									lowercase_	:   Dict  				=     output[output != -float("""inf"""  )]
									lowercase_	:   Tuple  				=     tf.cast(
									    tf.where(tf.not_equal(snake_case__,   tf.constant(-float("""inf"""  ),   dtype=tf.floataa  )  )  ),   dtype=tf.intaa,   )

									tf.debugging.assert_near(snake_case__,   snake_case__,   rtol=1E-12  )
									tf.debugging.assert_equal(snake_case__,   snake_case__  )




@require_tf
class     UpperCamelCase__					(     unittest.TestCase				, lowerCamelCase__				):






		'''simple docstring'''






		if is_tf_available():
				__a       :       Optional[int]       =	{
				    """AutoModelForCausalLM""": TFAutoModelForCausalLM,
				    """AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
				    """AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
				    """AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
				    """LogitsProcessorList""": TFLogitsProcessorList,
				    """MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
				    """create_tensor_fn""": tf.convert_to_tensor,
				    """floats_tensor""": floats_tensor,
				    """return_tensors""": """tf""",
				}






		@slow
		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									# TF-only test: tf.saved_model export
									lowercase_	:   Optional[Any]  				=     TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""  )
									lowercase_	:   int  				=     2
									lowercase_	:   Optional[Any]  				=     2
									class     UpperCamelCase__					(     tf.Module				):






											'''simple docstring'''






											def __init__(							self,   snake_case__  )       -> Any:






																		"""simple docstring"""
																		super(snake_case__,   self  ).__init__()
																		lowercase_	:   int  				=     model






											@tf.function(
											    input_signature=(
											        tf.TensorSpec((None, input_length),   tf.intaa,   name="""input_ids"""  ),
											        tf.TensorSpec((None, input_length),   tf.intaa,   name="""attention_mask"""  ),
											    ),   jit_compile=snake_case__,   )
											def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> int:






																		"""simple docstring"""
																		lowercase_	:   Tuple  				=     self.model.generate(
																		    input_ids=snake_case__,   attention_mask=snake_case__,   max_new_tokens=snake_case__,   return_dict_in_generate=snake_case__,   )
																		return {"sequences": outputs["sequences"]}




									lowercase_	:   Union[str, Any]  				=     [[2, 0], [1_02, 1_03]]
									lowercase_	:   Tuple  				=     [[1, 0], [1, 1]]
									lowercase_	:   int  				=     DummyModel(model=snake_case__  )
									with tempfile.TemporaryDirectory() as tmp_dir:
																tf.saved_model.save(snake_case__,   snake_case__,   signatures={"""serving_default""": dummy_model.serving}  )
																lowercase_	:   List[Any]  				=     tf.saved_model.load(snake_case__  ).signatures["""serving_default"""]
																for batch_size in range(1,   len(snake_case__  ) + 1  ):
																							lowercase_	:   str  				=     {
																							    """input_ids""": tf.constant(dummy_input_ids[:batch_size]  ),
																							    """attention_mask""": tf.constant(dummy_attention_masks[:batch_size]  ),
																							}
																							lowercase_	:   Optional[int]  				=     serving_func(**snake_case__  )["""sequences"""]
																							lowercase_	:   Optional[int]  				=     test_model.generate(**snake_case__,   max_new_tokens=snake_case__  )
																							tf.debugging.assert_equal(snake_case__,   snake_case__  )






		@slow
		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									# TF-only test: tf.saved_model export
									lowercase_	:   int  				=     TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""  )
									lowercase_	:   Tuple  				=     1
									lowercase_	:   Tuple  				=     2
									class     UpperCamelCase__					(     tf.Module				):






											'''simple docstring'''






											def __init__(							self,   snake_case__  )       -> List[str]:






																		"""simple docstring"""
																		super(snake_case__,   self  ).__init__()
																		lowercase_	:   Union[str, Any]  				=     model






											@tf.function(
											    input_signature=(
											        tf.TensorSpec((batch_size, None),   tf.intaa,   name="""input_ids"""  ),
											        tf.TensorSpec((batch_size, None),   tf.intaa,   name="""attention_mask"""  ),
											    ),   jit_compile=snake_case__,   )
											def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> Optional[Any]:






																		"""simple docstring"""
																		lowercase_	:   str  				=     self.model.generate(
																		    input_ids=snake_case__,   attention_mask=snake_case__,   max_new_tokens=snake_case__,   return_dict_in_generate=snake_case__,   )
																		return {"sequences": outputs["sequences"]}




									lowercase_	:   Union[str, Any]  				=     [[2], [1_02, 1_03]]
									lowercase_	:   List[str]  				=     [[1], [1, 1]]
									lowercase_	:   Union[str, Any]  				=     DummyModel(model=snake_case__  )
									with tempfile.TemporaryDirectory() as tmp_dir:
																tf.saved_model.save(snake_case__,   snake_case__,   signatures={"""serving_default""": dummy_model.serving}  )
																lowercase_	:   Optional[Any]  				=     tf.saved_model.load(snake_case__  ).signatures["""serving_default"""]
																for input_row in range(len(snake_case__  )  ):
																							lowercase_	:   List[str]  				=     {
																							    """input_ids""": tf.constant([dummy_input_ids[input_row]]  ),
																							    """attention_mask""": tf.constant([dummy_attention_masks[input_row]]  ),
																							}
																							lowercase_	:   Union[str, Any]  				=     serving_func(**snake_case__  )["""sequences"""]
																							lowercase_	:   Optional[Any]  				=     test_model.generate(**snake_case__,   max_new_tokens=snake_case__  )
																							tf.debugging.assert_equal(snake_case__,   snake_case__  )






		@slow
		@require_tensorflow_text
		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									# TF-only test: tf.saved_model export
									with tempfile.TemporaryDirectory() as tmp_dir:
																# file needed to load the TF tokenizer
																hf_hub_download(repo_id="""google/flan-t5-small""",   filename="""spiece.model""",   local_dir=snake_case__  )
																class     UpperCamelCase__					(     tf.keras.layers.Layer				):






																		'''simple docstring'''






																		def __init__(							self  )       -> Optional[Any]:






																									"""simple docstring"""
																									super().__init__()
																									lowercase_	:   Tuple  				=     text.SentencepieceTokenizer(
																									    model=tf.io.gfile.GFile(os.path.join(snake_case__,   """spiece.model"""  ),   """rb"""  ).read()  )
																									lowercase_	:   Dict  				=     TFAutoModelForSeqaSeqLM.from_pretrained("""hf-internal-testing/tiny-random-t5"""  )






																		def 						snake_case__				(							self,   snake_case__,   *snake_case__,   **snake_case__  )       -> Optional[Any]:






																									"""simple docstring"""
																									lowercase_	:   Union[str, Any]  				=     self.tokenizer.tokenize(snake_case__  )
																									lowercase_				,		lowercase_	:   Union[str, Any]  				=     text.pad_model_inputs(
																									    snake_case__,   max_seq_length=64,   pad_value=self.model.config.pad_token_id  )
																									lowercase_	:   str  				=     self.model.generate(input_ids=snake_case__,   attention_mask=snake_case__  )
																									return self.tokenizer.detokenize(snake_case__  )




																lowercase_	:   Optional[int]  				=     CompleteSentenceTransformer()
																lowercase_	:   Union[str, Any]  				=     tf.keras.layers.Input(shape=(1,),   dtype=tf.string,   name="""inputs"""  )
																lowercase_	:   Any  				=     complete_model(snake_case__  )
																lowercase_	:   Optional[Any]  				=     tf.keras.Model(snake_case__,   snake_case__  )
																keras_model.save(snake_case__  )






		def 						snake_case__				(							self  )       -> Optional[int]:






									"""simple docstring"""
									# Has PT equivalent: this test relies on random sampling
									lowercase_	:   Any  				=     {
									    """do_sample""": True,
									    """num_beams""": 1,
									    """top_p""": 0.7,
									    """top_k""": 10,
									    """temperature""": 0.7,
									}
									lowercase_	:   List[str]  				=     14

									lowercase_	:   Optional[Any]  				=     AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""  )
									lowercase_	:   Dict  				=     """Hello, my dog is cute and"""
									lowercase_	:   List[str]  				=     tokenizer(snake_case__,   return_tensors="""tf"""  )
									lowercase_	:   Tuple  				=     TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""  )

									lowercase_	:   int  				=     6_38
									# forces the generation to happen on CPU, to avoid GPU-related quirks
									with tf.device(""":/CPU:0"""  ):
																tf.random.set_seed(0  )
																lowercase_	:   Optional[int]  				=     model.generate(**snake_case__,   eos_token_id=snake_case__,   **snake_case__  )
									self.assertTrue(expectation == len(generated_tokens[0]  )  )

									lowercase_	:   int  				=     [6_38, 1_98]
									with tf.device(""":/CPU:0"""  ):
																tf.random.set_seed(0  )
																lowercase_	:   Optional[int]  				=     model.generate(**snake_case__,   eos_token_id=snake_case__,   **snake_case__  )
									self.assertTrue(expectation == len(generated_tokens[0]  )  )






		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									# Has PT equivalent: ample use of framework-specific code
									lowercase_	:   List[Any]  				=     AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bart"""  )
									lowercase_	:   int  				=     """Hugging Face is a technology company based in New York and Paris."""
									lowercase_	:   int  				=     bart_tokenizer(snake_case__,   return_tensors="""tf"""  ).input_ids
									lowercase_	:   Tuple  				=     TFBartForConditionalGeneration.from_pretrained("""hf-internal-testing/tiny-random-bart"""  )
									lowercase_	:   Union[str, Any]  				=     bart_model.generate(snake_case__  ).numpy()
									class     UpperCamelCase__					(     lowerCamelCase__				):






											'''simple docstring'''






											def 						snake_case__				(							self,   snake_case__,   snake_case__=None,   **snake_case__  )       -> str:






																		"""simple docstring"""
																		return super().call(snake_case__,   **snake_case__  )




									lowercase_	:   Tuple  				=     FakeBart.from_pretrained("""hf-internal-testing/tiny-random-bart"""  )
									lowercase_	:   str  				=     bart_model.generate(snake_case__,   foo="""bar"""  ).numpy()
									self.assertTrue(np.array_equal(snake_case__,   snake_case__  )  )
									class     UpperCamelCase__					(     bart_model.model.encoder.__class__				):






											'''simple docstring'''






											def 						snake_case__				(							self,   snake_case__,   **snake_case__  )       -> List[str]:






																		"""simple docstring"""
																		return super().call(snake_case__,   **snake_case__  )




									lowercase_	:   Optional[int]  				=     FakeEncoder(bart_model.config,   bart_model.model.shared  )
									lowercase_	:   Any  				=     fake_encoder

									# Normal generation still works (the output will be different because the encoder weights are different)
									lowercase_	:   Any  				=     bart_model.generate(snake_case__  ).numpy()
									with self.assertRaises(snake_case__  ):
																# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
																bart_model.generate(snake_case__,   foo="""bar"""  )