
"""simple docstring"""





import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors


@require_tokenizers
class A_      (lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Dict    	=						BartTokenizer
  SCREAMING_SNAKE_CASE__     :		str    	=						BartTokenizerFast
  SCREAMING_SNAKE_CASE__     :		Union[str, Any]    	=						True
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						filter_roberta_detectors







  def 			UpperCamelCase__   (						self    ):

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

     UpperCAmelCase_   :   int     	=       os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES["vocab_file"]    )
     UpperCAmelCase_   :   Optional[Any]     	=       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							,  **lowercase_    ):

     """simple docstring"""
     kwargs.update(self.special_tokens_map    )
     return self.tokenizer_class.from_pretrained(self.tmpdirname							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  **lowercase_    ):

     """simple docstring"""
     kwargs.update(self.special_tokens_map    )
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     return "lower newer", "lower newer"







  @cached_property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return BartTokenizer.from_pretrained("facebook/bart-large"    )







  @cached_property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return BartTokenizerFast.from_pretrained("facebook/bart-large"    )







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       ["A long paragraph for summarization.", "Another paragraph for summarization."]
     UpperCAmelCase_   :   int     	=       [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   str     	=       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    )
        UpperCAmelCase_   :   Union[str, Any]     	=       batch.input_ids.tolist()[0]
        self.assertListEqual(lowercase_							,  lowercase_    )
        # Test that special tokens are reset







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       ["A long paragraph for summarization.", "Another paragraph for summarization."]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   Dict     	=       tokenizer(lowercase_							,  padding=lowercase_							,  return_tensors="pt"    )
        # check if input_ids are returned and no labels
        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    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       [
         "Summary of the text.",
         "Another summary.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   List[Any]     	=       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    ):

     """simple docstring"""
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   Optional[int]     	=       tokenizer(
            ["I am a small frog" * 1024, "I am a small frog"]							,  padding=lowercase_							,  truncation=lowercase_							,  return_tensors="pt"    )
        self.assertIsInstance(lowercase_							,  lowercase_    )
        self.assertEqual(batch.input_ids.shape							,  (2, 1024)    )







  @require_torch
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       ["A long paragraph for summarization."]
     UpperCAmelCase_   :   List[str]     	=       [
         "Summary of the text.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_   :   Any     	=       tokenizer(lowercase_							,  return_tensors="pt"    )
        UpperCAmelCase_   :   int     	=       tokenizer(text_target=lowercase_							,  return_tensors="pt"    )
        UpperCAmelCase_   :   Optional[int]     	=       inputs["input_ids"]
        UpperCAmelCase_   :   Any     	=       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()    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
           UpperCAmelCase_   :   List[str]     	=       self.rust_tokenizer_class.from_pretrained(lowercase_							,  **lowercase_    )
           UpperCAmelCase_   :   Optional[int]     	=       self.tokenizer_class.from_pretrained(lowercase_							,  **lowercase_    )
           UpperCAmelCase_   :   Optional[Any]     	=       "A, <mask> AllenNLP sentence."
           UpperCAmelCase_   :   List[Any]     	=       tokenizer_r.encode_plus(lowercase_							,  add_special_tokens=lowercase_							,  return_token_type_ids=lowercase_    )
           UpperCAmelCase_   :   Optional[int]     	=       tokenizer_p.encode_plus(lowercase_							,  add_special_tokens=lowercase_							,  return_token_type_ids=lowercase_    )

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r["token_type_ids"]    )							,  sum(tokens_p["token_type_ids"]    )    )

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r["attention_mask"]    ) / len(tokens_r["attention_mask"]    )							,  sum(tokens_p["attention_mask"]    ) / len(tokens_p["attention_mask"]    )							,  )

           UpperCAmelCase_   :   Union[str, Any]     	=       tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]    )
           UpperCAmelCase_   :   Tuple     	=       tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]    )

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p["input_ids"]							,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )
           self.assertSequenceEqual(tokens_r["input_ids"]							,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )

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






'''simple docstring'''




import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax



@require_flax
class 			lowerCAmelCase					:



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Union[str, Any]   				=			self.prepare_init_args_and_inputs_for_common()

		lowerCAmelCase       :     Any   				=			self.model_class(**snake_case__     )
		lowerCAmelCase       :     int   				=			model.init(inputs_dict['prng_key']			,       inputs_dict['sample']     )
		jax.lax.stop_gradient(snake_case__     )

		lowerCAmelCase       :     Tuple   				=			model.apply(snake_case__			,       inputs_dict['sample']     )

		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     int   				=			output.sample

		self.assertIsNotNone(snake_case__     )
		lowerCAmelCase       :     List[str]   				=			inputs_dict['sample'].shape
		self.assertEqual(output.shape			,       snake_case__			,       'Input and output shapes do not match'     )



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Optional[int]   				=			self.prepare_init_args_and_inputs_for_common()

		lowerCAmelCase       :     Tuple   				=			16
		lowerCAmelCase       :     Dict   				=			(16, 32)

		lowerCAmelCase       :     Dict   				=			self.model_class(**snake_case__     )
		lowerCAmelCase       :     int   				=			model.init(inputs_dict['prng_key']			,       inputs_dict['sample']     )
		jax.lax.stop_gradient(snake_case__     )

		lowerCAmelCase       :     int   				=			model.apply(snake_case__			,       inputs_dict['sample']     )

		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     Dict   				=			output.sample

		self.assertIsNotNone(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			inputs_dict['sample'].shape
		self.assertEqual(output.shape			,       snake_case__			,       'Input and output shapes do not match'     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Any   				=			'kwargs' in inspect.signature(self.model_class.__init__     ).parameters
		lowerCAmelCase       :     Optional[Any]   				=			len(self.model_class._deprecated_kwargs     ) > 0

		if has_kwarg_in_model_class and not has_deprecated_kwarg:
			raise ValueError(
			    f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
			    ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
			    ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
			    ' [<deprecated_argument>]`'     )

		if not has_kwarg_in_model_class and has_deprecated_kwarg:
			raise ValueError(
			    f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
			    ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
			    f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
			    ' from `_deprecated_kwargs = [<deprecated_argument>]`'     )
