





'''simple docstring'''

import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class        UpperCAmelCase     (	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    CLIPTokenizer
 __lowercase       		=    CLIPTokenizerFast
 __lowercase       		=    True
 __lowercase       		=    {}
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
     super().setUp()

     # fmt: off
     A__    =      ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
     # fmt: on
     A__    =      dict(zip(lowercase_						,	range(len(lowercase_							)							)							)							)
     A__    =      ["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
     A__    =      {"unk_token": "<unk>"}

     A__    =      os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["vocab_file"]							)
     A__    =      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				:Optional[int]						,	**lowercase_				:List[str]							)->							str:
     kwargs.update(self.special_tokens_map							)
     return CLIPTokenizer.from_pretrained(self.tmpdirname						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	**lowercase_				:Optional[int]							)->							Tuple:
     kwargs.update(self.special_tokens_map							)
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:List[Any]							)->							List[str]:
     A__    =      "lower newer"
     A__    =      "lower newer"
     return input_text, output_text
 def 					UpperCAmelCase_							(    self				:int							)->							List[str]:
     A__    =      CLIPTokenizer(self.vocab_file						,	self.merges_file						,	**self.special_tokens_map							)
     A__    =      "lower newer"
     A__    =      ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
     A__    =      tokenizer.tokenize(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      tokens + [tokenizer.unk_token]
     A__    =      [10, 2, 16, 9, 3, 2, 16, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)						,	lowercase_							)
 @require_ftfy
 def 					UpperCAmelCase_							(    self				:Dict							)->							str:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
             A__    =      self.tokenizer_class.from_pretrained(lowercase_						,	**lowercase_							)
             A__    =      self.rust_tokenizer_class.from_pretrained(lowercase_						,	**lowercase_							)

             A__    =      "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
             A__    =      tokenizer_s.tokenize(lowercase_							)
             A__    =      tokenizer_r.tokenize(lowercase_							)

             self.assertListEqual(lowercase_						,	lowercase_							)

             # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
             # with Tilde) encoded in 2 different ways
             A__    =      "xa\u0303y" + " " + "x\xe3y"
             A__    =      tokenizer_s.tokenize(lowercase_							)
             A__    =      tokenizer_r.tokenize(lowercase_							)

             self.assertListEqual(lowercase_						,	lowercase_							)

             # Test that the tokenization is identical on unicode of space type
             A__    =      [
                 "\u0009",  # (horizontal tab, '\t')
                 "\u000B",  # (vertical tab)
                 "\u000C",  # (form feed)
                 "\u0020",  # (space, ' ')
                 "\u200E",  # (left-to-right mark):w
                 "\u200F",  # (right-to-left mark)
             ]
             for unicode_seq in spaces_unicodes:
                 A__    =      tokenizer_s.tokenize(lowercase_							)
                 A__    =      tokenizer_r.tokenize(lowercase_							)

                 self.assertListEqual(lowercase_						,	lowercase_							)

             # Test that the tokenization is identical on unicode of line break type
             A__    =      [
                 "\u000A",  # (line feed, '\n')
                 "\r\n",  # (carriage return and line feed, '\r\n')
                 "\u000D",  # (carriage return, '\r')
                 "\r",  # (carriage return, '\r')
                 "\u000D",  # (carriage return, '\r')
                 "\u2028",  # (line separator)
                 "\u2029",  # (paragraph separator)
                 # "\u0085", # (next line)
             ]

             # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
             # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
             # space (and thus into an empty list).

             for unicode_seq in line_break_unicodes:
                 A__    =      tokenizer_s.tokenize(lowercase_							)
                 A__    =      tokenizer_r.tokenize(lowercase_							)

                 self.assertListEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
     # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
             A__    =      "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
             A__    =      F"{text_of_1_token} {text_of_1_token}"

             A__    =      self.rust_tokenizer_class.from_pretrained(
                 lowercase_						,	use_fast=lowercase_						,	)
             A__    =      tokenizer_r(lowercase_						,	return_offsets_mapping=lowercase_						,	add_special_tokens=lowercase_							)
             self.assertEqual(encoding.offset_mapping[0]						,	(0, len(lowercase_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]						,	(len(lowercase_							) + 1, len(lowercase_							) + 1 + len(lowercase_							))						,	)

             A__    =      F" {text}"

             A__    =      self.rust_tokenizer_class.from_pretrained(
                 lowercase_						,	use_fast=lowercase_						,	)
             A__    =      tokenizer_r(lowercase_						,	return_offsets_mapping=lowercase_						,	add_special_tokens=lowercase_							)
             self.assertEqual(encoding.offset_mapping[0]						,	(1, 1 + len(lowercase_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]						,	(1 + len(lowercase_							) + 1, 1 + len(lowercase_							) + 1 + len(lowercase_							))						,	)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[Any]:
     # Test related to the breaking change introduced in transformers v4.17.0
     # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
     with self.assertRaises(lowercase_							) as context:
         self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"							)

     self.assertTrue(
         context.exception.args[0].startswith(
             "The `backend_tokenizer` provided does not match the expected format."							)							)
 @require_ftfy
 def 					UpperCAmelCase_							(    self				:str							)->							Any:
     super().test_tokenization_python_rust_equals()



 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Union[str, Any]:
     # CLIP always lower cases letters
     pass










'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.distilbert.modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertModel,
  )






class        UpperCAmelCase     :
 def __init__(    self				:str						,	lowercase_				:str						,	)->							str:
     A__    =      parent
     A__    =      13
     A__    =      7
     A__    =      True
     A__    =      True
     A__    =      False
     A__    =      True
     A__    =      99
     A__    =      32
     A__    =      2
     A__    =      4
     A__    =      37
     A__    =      "gelu"
     A__    =      0.1
     A__    =      0.1
     A__    =      5_12
     A__    =      16
     A__    =      2
     A__    =      0.0_2
     A__    =      3
     A__    =      4
     A__    =      None
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							int:
     A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size							)

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

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

     A__    =      DistilBertConfig(
         vocab_size=self.vocab_size						,	dim=self.hidden_size						,	n_layers=self.num_hidden_layers						,	n_heads=self.num_attention_heads						,	hidden_dim=self.intermediate_size						,	hidden_act=self.hidden_act						,	dropout=self.hidden_dropout_prob						,	attention_dropout=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	initializer_range=self.initializer_range						,	)

     return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Optional[int]						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:str							)->							List[str]:
     A__    =      TFDistilBertModel(config=lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}

     A__    =      model(lowercase_							)

     A__    =      [input_ids, input_mask]

     A__    =      model(lowercase_							)

     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:str						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
     A__    =      TFDistilBertForMaskedLM(config=lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)							)
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:str						,	lowercase_				:str						,	lowercase_				:Optional[int]						,	lowercase_				:str						,	lowercase_				:List[Any]						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
     A__    =      TFDistilBertForQuestionAnswering(config=lowercase_							)
     A__    =      {
         "input_ids": input_ids,
         "attention_mask": input_mask,
     }
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length)							)
     self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length)							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:Any						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]							)->							Any:
     A__    =      self.num_labels
     A__    =      TFDistilBertForSequenceClassification(lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Optional[Any]						,	lowercase_				:List[Any]						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:int						,	lowercase_				:Union[str, Any]							)->							str:
     A__    =      self.num_choices
     A__    =      TFDistilBertForMultipleChoice(lowercase_							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      {
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
     }
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Any						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:int						,	lowercase_				:List[Any]						,	lowercase_				:Tuple							)->							Tuple:
     A__    =      self.num_labels
     A__    =      TFDistilBertForTokenClassification(lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)							)



 def 					UpperCAmelCase_							(    self				:Any							)->							Union[str, Any]:
     A__    =      self.prepare_config_and_inputs()
     ((A__),     (A__),     (A__),     (A__),     (A__),     (A__))    =      config_and_inputs
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_tf
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (
     (
         TFDistilBertModel,
         TFDistilBertForMaskedLM,
         TFDistilBertForQuestionAnswering,
         TFDistilBertForSequenceClassification,
         TFDistilBertForTokenClassification,
         TFDistilBertForMultipleChoice,
     )
     if is_tf_available()
     else None
 )
 __lowercase       		=    (
     {
         """feature-extraction""": TFDistilBertModel,
         """fill-mask""": TFDistilBertForMaskedLM,
         """question-answering""": TFDistilBertForQuestionAnswering,
         """text-classification""": TFDistilBertForSequenceClassification,
         """token-classification""": TFDistilBertForTokenClassification,
         """zero-shot""": TFDistilBertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      TFDistilBertModelTester(self							)
     A__    =      ConfigTester(self						,	config_class=lowercase_						,	dim=37							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     self.config_tester.run_common_tests()
 def 					UpperCAmelCase_							(    self				:int							)->							Tuple:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_masked_lm(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							str:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_question_answering(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							int:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_token_classification(*lowercase_							)



 @slow
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
         A__    =      TFDistilBertModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)






@require_tf
class        UpperCAmelCase     (	unittest.TestCase    ):
 @slow
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Any:
     A__    =      TFDistilBertModel.from_pretrained("distilbert-base-uncased"							)
     A__    =      tf.constant([[0, 1, 2, 3, 4, 5]]							)
     A__    =      model(lowercase_							)[0]

     A__    =      [1, 6, 7_68]
     self.assertEqual(output.shape						,	lowercase_							)

     A__    =      tf.constant(
         [
             [
                 [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
                 [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
                 [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
             ]
         ]							)
     tf.debugging.assert_near(output[:, :3, :3]						,	lowercase_						,	atol=1E-4							)



