




import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM




@require_torch
class 		__A      :

						def __init__(		self   :Tuple    , __snake_case   :Tuple    , __snake_case   :List[str]=99    , __snake_case   :List[str]=13    , __snake_case   :int=16    , __snake_case   :str=7    , __snake_case   :List[str]=True    , __snake_case   :Optional[int]=True    , __snake_case   :Optional[int]=True    , __snake_case   :Union[str, Any]=False    , __snake_case   :str=True    , __snake_case   :Dict=2    , __snake_case   :Dict=32    , __snake_case   :str=4    , __snake_case   :Dict=4    , __snake_case   :str=30    , __snake_case   :Optional[int]=0    , __snake_case   :List[str]=1    , __snake_case   :Tuple=2    , __snake_case   :Optional[Any]=None    , ):
										'''simple docstring'''

										__magic_name__	:	int								=parent
										__magic_name__	:	Tuple								=batch_size
										__magic_name__	:	Dict								=decoder_seq_length
										# For common tests
										__magic_name__	:	Optional[Any]								=self.decoder_seq_length
										__magic_name__	:	str								=is_training
										__magic_name__	:	Optional[Any]								=use_attention_mask
										__magic_name__	:	int								=use_labels

										__magic_name__	:	Dict								=vocab_size
										__magic_name__	:	str								=d_model
										__magic_name__	:	Any								=d_model
										__magic_name__	:	Dict								=decoder_layers
										__magic_name__	:	Dict								=decoder_layers
										__magic_name__	:	Optional[int]								=decoder_ffn_dim
										__magic_name__	:	str								=decoder_attention_heads
										__magic_name__	:	Union[str, Any]								=decoder_attention_heads
										__magic_name__	:	str								=eos_token_id
										__magic_name__	:	Dict								=bos_token_id
										__magic_name__	:	Dict								=pad_token_id
										__magic_name__	:	Tuple								=decoder_start_token_id
										__magic_name__	:	Optional[Any]								=use_cache
										__magic_name__	:	int								=max_position_embeddings

										__magic_name__	:	List[str]								=None
										__magic_name__	:	str								=decoder_seq_length
										__magic_name__	:	List[str]								=2
										__magic_name__	:	str								=1

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=ids_tensor([self.batch_size, self.decoder_seq_length]    , self.vocab_size )

										__magic_name__	:	Optional[Any]								=None
										if self.use_attention_mask:
														__magic_name__	:	Tuple								=ids_tensor([self.batch_size, self.decoder_seq_length]    , vocab_size=2 )

										__magic_name__	:	Optional[Any]								=None
										if self.use_labels:
														__magic_name__	:	Optional[int]								=ids_tensor([self.batch_size, self.decoder_seq_length]    , self.vocab_size )

										__magic_name__	:	Union[str, Any]								=TrOCRConfig(
										    vocab_size=self.vocab_size    , d_model=self.d_model    , decoder_layers=self.decoder_layers    , decoder_ffn_dim=self.decoder_ffn_dim    , decoder_attention_heads=self.decoder_attention_heads    , eos_token_id=self.eos_token_id    , bos_token_id=self.bos_token_id    , use_cache=self.use_cache    , pad_token_id=self.pad_token_id    , decoder_start_token_id=self.decoder_start_token_id    , max_position_embeddings=self.max_position_embeddings    , )

										return (config, input_ids, attention_mask, lm_labels)

						def     A__						(		self   :Union[str, Any]    , __snake_case   :Any    , __snake_case   :Optional[int]    , __snake_case   :Optional[Any]    , __snake_case   :Tuple    , ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=True
										__magic_name__	:	Union[str, Any]								=TrOCRDecoder(config=UpperCamelCase_ ).to(UpperCamelCase_ ).eval()
										__magic_name__	:	int								=input_ids[:2]

										input_ids[input_ids == 0] += 1
										# first forward pass
										__magic_name__	:	Optional[int]								=model(UpperCamelCase_    , use_cache=UpperCamelCase_ )
										__magic_name__	:	Optional[int]								=model(UpperCamelCase_ )
										__magic_name__	:	Optional[int]								=model(UpperCamelCase_    , use_cache=UpperCamelCase_ )

										self.parent.assertTrue(len(UpperCamelCase_ ) == len(UpperCamelCase_ ) )
										self.parent.assertTrue(len(UpperCamelCase_ ) == len(UpperCamelCase_ ) + 1 )

										__magic_name__	:	List[Any]								=outputs['past_key_values']

										# create hypothetical next token and extent to next_input_ids
										__magic_name__	:	int								=ids_tensor((2, 1)    , config.vocab_size - 1 ) + 1

										# append to next input_ids and
										__magic_name__	:	Dict								=torch.cat([input_ids, next_tokens]    , dim=-1 )

										__magic_name__	:	int								=model(UpperCamelCase_ )['last_hidden_state']
										__magic_name__	:	Dict								=model(UpperCamelCase_    , past_key_values=UpperCamelCase_ )['last_hidden_state']

										# select random slice
										__magic_name__	:	List[str]								=ids_tensor((1,)    , output_from_past.shape[-1] ).item()
										__magic_name__	:	Union[str, Any]								=output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
										__magic_name__	:	Optional[int]								=output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										assert torch.allclose(UpperCamelCase_    , UpperCamelCase_    , atol=1E-3 )


						def     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.prepare_config_and_inputs()
										__magic_name__	:	Optional[int]								=config_and_inputs

										__magic_name__	:	int								={'input_ids': input_ids, 'attention_mask': attention_mask}
										return config, inputs_dict




@require_torch
class 		__A      (			UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
						UpperCamelCase     						=					(TrOCRForCausalLM,) if is_torch_available() else ()
						UpperCamelCase     						=					{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
						UpperCamelCase     						=					True
						UpperCamelCase     						=					False

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=TrOCRStandaloneDecoderModelTester(self    , is_training=UpperCamelCase_ )
										__magic_name__	:	int								=ConfigTester(self    , config_class=UpperCamelCase_ )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										pass

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										pass

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										pass

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase_ )

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										return


						@unittest.skip("""The model doesn\'t support left padding""" )  # and it's not used enough to be worth fixing :)
						def     A__						(		self   :int ):
										'''simple docstring'''

										pass










import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin




class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					BlenderbotSmallTokenizer
						UpperCamelCase     						=					False

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										super().setUp()

										__magic_name__	:	Tuple								=["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
										__magic_name__	:	Optional[Any]								=dict(zip(__snake_case    , range(len(__snake_case ) ) ) )

										__magic_name__	:	List[Any]								=["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
										__magic_name__	:	int								={"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

										__magic_name__	:	Dict								=os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["""vocab_file"""] )
										__magic_name__	:	int								=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(__snake_case ) + """\n""" )
										with open(self.merges_file    , """w"""    , encoding="""utf-8""" ) as fp:
														fp.write("""\n""".join(__snake_case ) )

						def     A__						(		self   :Union[str, Any]    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										kwargs.update(self.special_tokens_map )
										return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname    , **__snake_case )

						def     A__						(		self   :str    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								="""adapt act apte"""
										__magic_name__	:	str								="""adapt act apte"""
										return input_text, output_text

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=BlenderbotSmallTokenizer(self.vocab_file    , self.merges_file    , **self.special_tokens_map )
										__magic_name__	:	List[str]								="""adapt act apte"""
										__magic_name__	:	Optional[int]								=["""adapt""", """act""", """ap@@""", """te"""]
										__magic_name__	:	List[Any]								=tokenizer.tokenize(__snake_case )
										self.assertListEqual(__snake_case    , __snake_case )

										__magic_name__	:	int								=[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

										__magic_name__	:	Any								=[0, 1, 2, 3, 4, 5]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case )    , __snake_case )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Any								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										assert tok("""sam""" ).input_ids == [13_84]
										__magic_name__	:	Tuple								="""I am a small frog."""
										__magic_name__	:	Tuple								=tok([src_text]    , padding=__snake_case    , truncation=__snake_case )["""input_ids"""]
										__magic_name__	:	Optional[Any]								=tok.batch_decode(__snake_case    , skip_special_tokens=__snake_case    , clean_up_tokenization_spaces=__snake_case )[0]
										assert src_text != decoded  # I wish it did!
										assert decoded == "i am a small frog ."


						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										__magic_name__	:	int								="""I am a small frog ."""
										__magic_name__	:	List[Any]								="""."""
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]

										assert encoded[-1] == encoded_dot[0]




