from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
      import tensorflow as tf

      from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class    snake_case       :
  lowercase_								=  BlenderbotSmallConfig
  lowercase_								=  {}
  lowercase_								=  'gelu'




  def __init__(   self  :						List[Any]  ,			a_  :						int  ,			a_  :						Any=13  ,			a_  :						List[str]=7  ,			a_  :						Optional[int]=True  ,			a_  :						Tuple=False  ,			a_  :						Optional[int]=99  ,			a_  :						Tuple=32  ,			a_  :						Union[str, Any]=2  ,			a_  :						Union[str, Any]=4  ,			a_  :						str=37  ,			a_  :						List[Any]=0.1  ,			a_  :						int=0.1  ,			a_  :						List[Any]=20  ,			a_  :						Optional[Any]=2  ,			a_  :						List[str]=1  ,			a_  :						Tuple=0  ,			)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		parent
       SCREAMING_SNAKE_CASE__					:					Tuple      =		batch_size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		seq_length
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		is_training
       SCREAMING_SNAKE_CASE__					:					Tuple      =		use_labels
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		vocab_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		hidden_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_hidden_layers
       SCREAMING_SNAKE_CASE__					:					int      =		num_attention_heads
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		intermediate_size

       SCREAMING_SNAKE_CASE__					:					Tuple      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		max_position_embeddings
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		eos_token_id
       SCREAMING_SNAKE_CASE__					:					Any      =		pad_token_id
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		bos_token_id




  def 						__lowercase(   self  :						Optional[int]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		ids_tensor([self.batch_size, self.seq_length - 1]  ,			self.vocab_size					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)  ,			1					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		tf.concat([input_ids, eos_tensor]  ,			axis=1					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.config_cls(
           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_ids=[2]  ,			bos_token_id=self.bos_token_id  ,			pad_token_id=self.pad_token_id  ,			decoder_start_token_id=self.pad_token_id  ,			**self.config_updates  ,			)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		prepare_blenderbot_small_inputs_dict(a_  ,			a_  ,			a_					)
       return config, inputs_dict




  def 						__lowercase(   self  :						Dict  ,			a_  :						Dict  ,			a_  :						str					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		TFBlenderbotSmallModel(config=a_					).get_decoder()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		inputs_dict['input_ids']

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		input_ids[:1, :]
       SCREAMING_SNAKE_CASE__					:					List[str]      =		inputs_dict['attention_mask'][:1, :]
       SCREAMING_SNAKE_CASE__					:					Any      =		inputs_dict['head_mask']
       SCREAMING_SNAKE_CASE__					:					str      =		1

       # first forward pass
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(a_  ,			attention_mask=a_  ,			head_mask=a_  ,			use_cache=a_					)

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		ids_tensor((self.batch_size, 3)  ,			config.vocab_size					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		tf.cast(ids_tensor((self.batch_size, 3)  ,			2					)  ,			tf.inta					)

       # append to next input_ids and
       SCREAMING_SNAKE_CASE__					:					List[str]      =		tf.concat([input_ids, next_tokens]  ,			axis=-1					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		tf.concat([attention_mask, next_attn_mask]  ,			axis=-1					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		model(a_  ,			attention_mask=a_					)[0]
       SCREAMING_SNAKE_CASE__					:					int      =		model(a_  ,			attention_mask=a_  ,			past_key_values=a_					)[0]

       self.parent.assertEqual(next_tokens.shape[1]  ,			output_from_past.shape[1]					)

       # select random slice
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		int(ids_tensor((1,)  ,			output_from_past.shape[-1]					)					)
       SCREAMING_SNAKE_CASE__					:					Any      =		output_from_no_past[:, -3:, random_slice_idx]
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(a_  ,			a_  ,			rtol=1e-3					)







def      _a					(   lowercase__	:      Any  ,	lowercase__	:      Any  ,	lowercase__	:      List[str]  ,	lowercase__	:      Any=None  ,	lowercase__	:      str=None  ,	lowercase__	:      int=None  ,	lowercase__	:      str=None  ,	lowercase__	:      Tuple=None  ,	):







     '''simple docstring'''


     if attention_mask is None:
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		tf.cast(tf.math.not_equal(lowercase__  ,	config.pad_token_id			)  ,	tf.inta			)
     if decoder_attention_mask is None:
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape  ,	dtype=tf.inta			),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,	config.pad_token_id			)  ,	tf.inta			),
              ]  ,	axis=-1  ,	)
     if head_mask is None:
          SCREAMING_SNAKE_CASE__					:					Tuple      =		tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
     if decoder_head_mask is None:
          SCREAMING_SNAKE_CASE__					:					List[str]      =		tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
     if cross_attn_head_mask is None:
          SCREAMING_SNAKE_CASE__					:					Tuple      =		tf.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": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }

@require_tf
class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  (
      (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
  )
  lowercase_								=  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
  lowercase_								=  (
      {
          'conversational': TFBlenderbotSmallForConditionalGeneration,
          'feature-extraction': TFBlenderbotSmallModel,
          'summarization': TFBlenderbotSmallForConditionalGeneration,
          'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
          'translation': TFBlenderbotSmallForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  lowercase_								=  True
  lowercase_								=  False
  lowercase_								=  False




  def 						__lowercase(   self  :						Any					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		TFBlenderbotSmallModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		ConfigTester(self  ,			config_class=a_					)




  def 						__lowercase(   self  :						Optional[Any]					)->							Dict:




       """simple docstring"""
       self.config_tester.run_common_tests()




  def 						__lowercase(   self  :						List[str]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*a_					)




@require_tokenizers
@require_tf
class    snake_case       (   unittest.TestCase    ):
  lowercase_								=  [
      'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
      '  i\'m going to throw up.\nand why is that?'
  ]
  lowercase_								=  'facebook/blenderbot_small-90M'




  @cached_property
  def 						__lowercase(   self  :						int					)->							Any:




       """simple docstring"""
       # use "old" tokenizer here because of bug when downloading new tokenizer
       return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'					)




  @cached_property
  def 						__lowercase(   self  :						Optional[int]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
       return model




  @slow
  def 						__lowercase(   self  :						str					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.tokenizer(self.src_text  ,			return_tensors='tf'					)
       SCREAMING_SNAKE_CASE__					:					int      =		self.model.generate(
           model_inputs.input_ids  ,			attention_mask=model_inputs.attention_mask  ,			num_beams=2  ,			use_cache=a_  ,			)
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.tokenizer.batch_decode(generated_ids.numpy()  ,			skip_special_tokens=a_					)[0]
       assert generated_words in (
           "i don't know. i just feel like i'm going to throw up. it's not fun.",
           "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
           "i'm not sure. i just feel like i've been in a bad situation.",
       )









'''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 		a							(  _lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						CLIPTokenizer
				snake_case_           =						CLIPTokenizerFast
				snake_case_           =						True
				snake_case_           =						{}
				snake_case_           =						False


				def     A_					(						self       :   List[Any]   ):
									super().setUp()

									# fmt: off
									snake_case_		     =							['''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
									snake_case_		     =							dict(zip(lowercase_			,							range(len(lowercase_   )   )   )   )
									snake_case_		     =							['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
									snake_case_		     =							{'''unk_token''': '''<unk>'''}

									snake_case_		     =							os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES['''vocab_file''']   )
									snake_case_		     =							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     A_					(						self       :   Tuple			,							**lowercase_       :   Tuple   ):
									kwargs.update(self.special_tokens_map   )
									return CLIPTokenizer.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   List[str]			,							**lowercase_       :   Dict   ):
									kwargs.update(self.special_tokens_map   )
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,							**lowercase_   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   Optional[int]   ):
									snake_case_		     =							'''lower newer'''
									snake_case_		     =							'''lower newer'''
									return input_text, output_text


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							CLIPTokenizer(self.vocab_file			,							self.merges_file			,							**self.special_tokens_map   )
									snake_case_		     =							'''lower newer'''
									snake_case_		     =							['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
									snake_case_		     =							tokenizer.tokenize(lowercase_   )
									self.assertListEqual(lowercase_			,							lowercase_   )

									snake_case_		     =							tokens + [tokenizer.unk_token]
									snake_case_		     =							[10, 2, 16, 9, 3, 2, 16, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_   )			,							lowercase_   )


				@require_ftfy
				def     A_					(						self       :   Optional[Any]   ):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
																			snake_case_		     =							self.tokenizer_class.from_pretrained(lowercase_			,							**lowercase_   )
																			snake_case_		     =							self.rust_tokenizer_class.from_pretrained(lowercase_			,							**lowercase_   )

																			snake_case_		     =							'''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
																			snake_case_		     =							tokenizer_s.tokenize(lowercase_   )
																			snake_case_		     =							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
																			snake_case_		     =							'''xa\u0303y''' + ''' ''' + '''x\xe3y'''
																			snake_case_		     =							tokenizer_s.tokenize(lowercase_   )
																			snake_case_		     =							tokenizer_r.tokenize(lowercase_   )

																			self.assertListEqual(lowercase_			,							lowercase_   )

																			# Test that the tokenization is identical on unicode of space type
																			snake_case_		     =							[
																			    '''\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:
																								snake_case_		     =							tokenizer_s.tokenize(lowercase_   )
																								snake_case_		     =							tokenizer_r.tokenize(lowercase_   )

																								self.assertListEqual(lowercase_			,							lowercase_   )

																			# Test that the tokenization is identical on unicode of line break type
																			snake_case_		     =							[
																			    '''\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:
																								snake_case_		     =							tokenizer_s.tokenize(lowercase_   )
																								snake_case_		     =							tokenizer_r.tokenize(lowercase_   )

																								self.assertListEqual(lowercase_			,							lowercase_   )


				def     A_					(						self       :   List[str]   ):
									# 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})"   ):
																			snake_case_		     =							'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																			snake_case_		     =							F"{text_of_1_token} {text_of_1_token}"

																			snake_case_		     =							self.rust_tokenizer_class.from_pretrained(
																			    lowercase_			,							use_fast=lowercase_			,							)
																			snake_case_		     =							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_   ))			,							)

																			snake_case_		     =							F" {text}"

																			snake_case_		     =							self.rust_tokenizer_class.from_pretrained(
																			    lowercase_			,							use_fast=lowercase_			,							)
																			snake_case_		     =							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     A_					(						self       :   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     A_					(						self       :   List[str]   ):
									super().test_tokenization_python_rust_equals()


				def     A_					(						self       :   List[Any]   ):
									# CLIP always lower cases letters
									pass

