from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel



@require_tf
class      _A	:




    """simple docstring"""




    UpperCamelCase_	:					Optional[Any]							=					BlenderbotConfig
    UpperCamelCase_	:					List[Any]							=					{}
    UpperCamelCase_	:					int							=					'''gelu'''
    def __init__(		self							:     Any	,      A_							:     str	,      A_							:     Any=13	,      A_							:     List[str]=7	,      A_							:     Any=True	,      A_							:     Optional[Any]=False	,      A_							:     Tuple=99	,      A_							:     Optional[int]=32	,      A_							:     str=2	,      A_							:     Dict=4	,      A_							:     Dict=37	,      A_							:     Tuple=0.1	,      A_							:     Optional[int]=0.1	,      A_							:     Optional[Any]=20	,      A_							:     Optional[int]=2	,      A_							:     Union[str, Any]=1	,      A_							:     int=0	,      )    ->       Tuple:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							seq_length
         __snake_case				=							is_training
         __snake_case				=							use_labels
         __snake_case				=							vocab_size
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size

         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							max_position_embeddings
         __snake_case				=							eos_token_id
         __snake_case				=							pad_token_id
         __snake_case				=							bos_token_id
    def  lowercase					(		self							:     Any    )    ->       Optional[Any]:
         __snake_case				=							ids_tensor([self.batch_size, self.seq_length - 1]	,      self.vocab_size    )
         __snake_case				=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )	,      1    )
         __snake_case				=							tf.concat([input_ids, eos_tensor]	,      axis=1    )

         __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size    )

         __snake_case				=							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	,      )
         __snake_case				=							prepare_blenderbot_inputs_dict(A_	,      A_	,      A_    )
         return config, inputs_dict







    def  lowercase					(		self							:     Any	,      A_							:     int	,      A_							:     Tuple    )    ->       List[Any]:
         __snake_case				=							TFBlenderbotModel(config=A_    ).get_decoder()
         __snake_case				=							inputs_dict['''input_ids''']

         __snake_case				=							input_ids[:1, :]
         __snake_case				=							inputs_dict['''attention_mask'''][:1, :]
         __snake_case				=							inputs_dict['''head_mask''']
         __snake_case				=							1

         # first forward pass
         __snake_case				=							model(A_	,      attention_mask=A_	,      head_mask=A_	,      use_cache=A_    )

         __snake_case		,	__snake_case				=							outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         __snake_case				=							ids_tensor((self.batch_size, 3)	,      config.vocab_size    )
         __snake_case				=							tf.cast(ids_tensor((self.batch_size, 3)	,      2    )	,      tf.inta    )

         # append to next input_ids and
         __snake_case				=							tf.concat([input_ids, next_tokens]	,      axis=-1    )
         __snake_case				=							tf.concat([attention_mask, next_attn_mask]	,      axis=-1    )

         __snake_case				=							model(A_	,      attention_mask=A_    )[0]
         __snake_case				=							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
         __snake_case				=							int(ids_tensor((1,)	,      output_from_past.shape[-1]    )    )
         __snake_case				=							output_from_no_past[:, -3:, random_slice_idx]
         __snake_case				=							output_from_past[:, :, random_slice_idx]

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






def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case=None,      snake_case=None,      snake_case=None,      snake_case=None,      snake_case=None,      ):
     if attention_mask is None:
          __snake_case				=							tf.cast(tf.math.not_equal(snake_case,      config.pad_token_id),      tf.inta)
     if decoder_attention_mask is None:
          __snake_case				=							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:
          __snake_case				=							tf.ones((config.encoder_layers, config.encoder_attention_heads))
     if decoder_head_mask is None:
          __snake_case				=							tf.ones((config.decoder_layers, config.decoder_attention_heads))
     if cross_attn_head_mask is None:
          __snake_case				=							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      _A	(						_UpperCAmelCase							, _UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[str]							=					(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
    UpperCamelCase_	:					Optional[int]							=					(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
    UpperCamelCase_	:					Optional[Any]							=					(
        {
            '''conversational''': TFBlenderbotForConditionalGeneration,
            '''feature-extraction''': TFBlenderbotModel,
            '''summarization''': TFBlenderbotForConditionalGeneration,
            '''text2text-generation''': TFBlenderbotForConditionalGeneration,
            '''translation''': TFBlenderbotForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    UpperCamelCase_	:					Optional[Any]							=					True
    UpperCamelCase_	:					Any							=					False
    UpperCamelCase_	:					Optional[Any]							=					False
    def  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							TFBlenderbotModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         self.config_tester.run_common_tests()







    def  lowercase					(		self							:     List[str]    )    ->       Tuple:
         __snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*A_    )






@require_tokenizers
@require_tf
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[Any]							=					['''My friends are cool but they eat too many carbs.''']
    UpperCamelCase_	:					Any							=					'''facebook/blenderbot-400M-distill'''
    @cached_property
    def  lowercase					(		self							:     str    )    ->       Optional[int]:
         return BlenderbotTokenizer.from_pretrained(self.model_name    )
    @cached_property
    def  lowercase					(		self							:     Tuple    )    ->       List[str]:
         __snake_case				=							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
         return model







    @slow
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         __snake_case				=							self.tokenizer(self.src_text	,      return_tensors='''tf'''    )
         __snake_case				=							self.model.generate(
             model_inputs.input_ids	,      )
         __snake_case				=							self.tokenizer.batch_decode(generated_ids.numpy()	,      skip_special_tokens=A_    )[0]
         assert (
             generated_words
             == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
         )
"""simple docstring"""



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import (
           AutoProcessor,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     str    )    ->       str:
         __snake_case				=							tempfile.mkdtemp()

         __snake_case				=							BlipImageProcessor()
         __snake_case				=							GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''    )
         __snake_case				=							BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )

         __snake_case				=							InstructBlipProcessor(A_	,      A_	,      A_    )

         processor.save_pretrained(self.tmpdirname    )
    def  lowercase					(		self							:     Tuple	,      **A_							:     str    )    ->       List[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).tokenizer
    def  lowercase					(		self							:     Union[str, Any]	,      **A_							:     Tuple    )    ->       Union[str, Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).image_processor
    def  lowercase					(		self							:     Union[str, Any]	,      **A_							:     Tuple    )    ->       Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).qformer_tokenizer
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Tuple:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

         __snake_case				=							[Image.fromarray(np.moveaxis(A_	,      0	,      -1    )    ) for x in image_inputs]

         return image_inputs
    def  lowercase					(		self							:     int    )    ->       Dict:
         __snake_case				=							InstructBlipProcessor(
             tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()	,      qformer_tokenizer=self.get_qformer_tokenizer()	,      )
         processor.save_pretrained(self.tmpdirname    )

         __snake_case				=							self.get_tokenizer(bos_token='''(BOS)'''	,      eos_token='''(EOS)'''    )
         __snake_case				=							self.get_image_processor(do_normalize=A_	,      padding_value=1.0    )

         __snake_case				=							InstructBlipProcessor.from_pretrained(
             self.tmpdirname	,      bos_token='''(BOS)'''	,      eos_token='''(EOS)'''	,      do_normalize=A_	,      padding_value=1.0    )

         self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer	,      A_    )

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
         self.assertIsInstance(processor.qformer_tokenizer	,      A_    )
    def  lowercase					(		self							:     int    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      return_tensors='''np'''    )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1E-2    )
    def  lowercase					(		self							:     List[str]    )    ->       Optional[int]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_	,      return_token_type_ids=A_    )
         __snake_case				=							qformer_tokenizer(A_	,      return_token_type_ids=A_    )

         for key in encoded_tokens.keys():
              self.assertListEqual(encoded_tokens[key]	,      encoded_processor[key]    )

         for key in encoded_tokens_qformer.keys():
              self.assertListEqual(encoded_tokens_qformer[key]	,      encoded_processor['''qformer_''' + key]    )
    def  lowercase					(		self							:     List[str]    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(
             list(inputs.keys()    )	,      ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']	,      )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     str    )    ->       Union[str, Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )







    def  lowercase					(		self							:     int    )    ->       List[str]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(
             list(inputs.keys()    )	,      ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']	,      )