from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFMarianModel, TFMarianMTModel




@require_tf
class  __lowerCAmelCase	:



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			MarianConfig
 _lowerCamelCase    :						Tuple					     =			{}
 _lowerCamelCase    :						str					     =			'gelu'

 def __init__(      self							:		Any							,      lowerCamelCase							:		str							,      lowerCamelCase							:		List[str]=1_3							,      lowerCamelCase							:		Any=7							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		int=False							,      lowerCamelCase							:		str=9_9							,      lowerCamelCase							:		Optional[int]=3_2							,      lowerCamelCase							:		Tuple=2							,      lowerCamelCase							:		int=4							,      lowerCamelCase							:		Any=3_7							,      lowerCamelCase							:		Union[str, Any]=0.1							,      lowerCamelCase							:		Dict=0.1							,      lowerCamelCase							:		int=2_0							,      lowerCamelCase							:		str=2							,      lowerCamelCase							:		Any=1							,      lowerCamelCase							:		List[Any]=0							,      )			->				Optional[int]:
        _UpperCamelCase  :							int	    =	parent
        _UpperCamelCase  :							List[Any]	    =	batch_size
        _UpperCamelCase  :							Any	    =	seq_length
        _UpperCamelCase  :							Union[str, Any]	    =	is_training
        _UpperCamelCase  :							str	    =	use_labels
        _UpperCamelCase  :							Union[str, Any]	    =	vocab_size
        _UpperCamelCase  :							int	    =	hidden_size
        _UpperCamelCase  :							Union[str, Any]	    =	num_hidden_layers
        _UpperCamelCase  :							Union[str, Any]	    =	num_attention_heads
        _UpperCamelCase  :							Optional[Any]	    =	intermediate_size

        _UpperCamelCase  :							List[Any]	    =	hidden_dropout_prob
        _UpperCamelCase  :							str	    =	attention_probs_dropout_prob
        _UpperCamelCase  :							List[str]	    =	max_position_embeddings
        _UpperCamelCase  :							Dict	    =	eos_token_id
        _UpperCamelCase  :							str	    =	pad_token_id
        _UpperCamelCase  :							Dict	    =	bos_token_id

 def        __snake_case       (      self							:		Optional[Any]	)			->				Optional[int]:
        _UpperCamelCase  :							Dict	    =	ids_tensor([self.batch_size, self.seq_length - 1]							,      self.vocab_size	)
        _UpperCamelCase  :							Optional[Any]	    =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)							,      1	)
        _UpperCamelCase  :							Any	    =	tf.concat([input_ids, eos_tensor]							,      axis=1	)

        _UpperCamelCase  :							Optional[int]	    =	ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size	)

        _UpperCamelCase  :							Optional[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							,      )
        _UpperCamelCase  :							int	    =	prepare_marian_inputs_dict(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
        return config, inputs_dict

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		List[Any]							,      lowerCamelCase							:		Dict	)			->				List[str]:
        _UpperCamelCase  :							Any	    =	TFMarianModel(config=lowerCamelCase	).get_decoder()
        _UpperCamelCase  :							Any	    =	inputs_dict['input_ids']

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

        # first forward pass
        _UpperCamelCase  :							Tuple	    =	model(lowerCamelCase							,      attention_mask=lowerCamelCase							,      head_mask=lowerCamelCase							,      use_cache=lowerCamelCase	)

        _UpperCamelCase    ,   _UpperCamelCase  :							List[str]	    =	outputs.to_tuple()

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

        # append to next input_ids and
        _UpperCamelCase  :							Dict	    =	tf.concat([input_ids, next_tokens]							,      axis=-1	)
        _UpperCamelCase  :							List[Any]	    =	tf.concat([attention_mask, next_attn_mask]							,      axis=-1	)

        _UpperCamelCase  :							str	    =	model(lowerCamelCase							,      attention_mask=lowerCamelCase	)[0]
        _UpperCamelCase  :							Union[str, Any]	    =	model(lowerCamelCase							,      attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase	)[0]

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

        # select random slice
        _UpperCamelCase  :							Dict	    =	int(ids_tensor((1,)							,      output_from_past.shape[-1]	)	)
        _UpperCamelCase  :							Dict	    =	output_from_no_past[:, -3:, random_slice_idx]
        _UpperCamelCase  :							Dict	    =	output_from_past[:, :, random_slice_idx]

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







def SCREAMING_SNAKE_CASE					(  lowercase_:						Any		,    lowercase_:						Optional[Any]		,    lowercase_:						Optional[int]		,    lowercase_:						Dict=None		,    lowercase_:						Union[str, Any]=None		,    lowercase_:						int=None		,    lowercase_:						List[Any]=None		,    lowercase_:						Union[str, Any]=None		,    ):
       if attention_mask is None:
              _UpperCamelCase  :							Dict	    =	tf.cast(tf.math.not_equal(lowercase_		,    config.pad_token_id					)		,    tf.inta					)
       if decoder_attention_mask is None:
              _UpperCamelCase  :							Union[str, 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:
              _UpperCamelCase  :							str	    =	tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
       if decoder_head_mask is None:
              _UpperCamelCase  :							Dict	    =	tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
       if cross_attn_head_mask is None:
              _UpperCamelCase  :							int	    =	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  __lowerCAmelCase	(							a_,						a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Any					     =			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
 _lowerCamelCase    :						Union[str, Any]					     =			(TFMarianMTModel,) if is_tf_available() else ()
 _lowerCamelCase    :						List[Any]					     =			(
     {
         'conversational': TFMarianMTModel,
         'feature-extraction': TFMarianModel,
         'summarization': TFMarianMTModel,
         'text2text-generation': TFMarianMTModel,
         'translation': TFMarianMTModel,
     }
     if is_tf_available()
     else {}
 )
 _lowerCamelCase    :						List[Any]					     =			True
 _lowerCamelCase    :						int					     =			False
 _lowerCamelCase    :						int					     =			False

 def        __snake_case       (      self							:		Any	)			->				Union[str, Any]:
        _UpperCamelCase  :							Optional[Any]	    =	TFMarianModelTester(self	)
        _UpperCamelCase  :							Any	    =	ConfigTester(self							,      config_class=lowerCamelCase	)

 def        __snake_case       (      self							:		int	)			->				str:
        self.config_tester.run_common_tests()

 def        __snake_case       (      self							:		Optional[int]	)			->				Union[str, Any]:
        _UpperCamelCase  :							List[str]	    =	self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase	)







@require_tf
class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			1_000  # show more chars for failing integration tests

 @classmethod
 def        __snake_case       (      cls							:		List[Any]	)			->				None:
        _UpperCamelCase  :							Any	    =	f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
        return cls

 @cached_property
 def        __snake_case       (      self							:		List[Any]	)			->				MarianTokenizer:
        return AutoTokenizer.from_pretrained(self.model_name	)

 @property
 def        __snake_case       (      self							:		Optional[int]	)			->				int:
        return self.tokenizer.eos_token_id

 @cached_property
 def        __snake_case       (      self							:		Tuple	)			->				str:
        warnings.simplefilter('error'	)
        _UpperCamelCase  :							TFMarianMTModel	    =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
        assert isinstance(lowerCamelCase							,      lowerCamelCase	)
        _UpperCamelCase  :							Dict	    =	model.config
        self.assertListEqual(c.bad_words_ids							,      [[c.pad_token_id]]	)
        self.assertEqual(c.max_length							,      5_1_2	)
        self.assertEqual(c.decoder_start_token_id							,      c.pad_token_id	)
        return model

 def        __snake_case       (      self							:		Dict							,      **lowerCamelCase							:		str	)			->				List[Any]:
        _UpperCamelCase  :							List[Any]	    =	self.translate_src_text(**lowerCamelCase	)
        self.assertListEqual(self.expected_text							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[int]							,      **lowerCamelCase							:		Dict	)			->				int:
        _UpperCamelCase  :							str	    =	self.tokenizer(self.src_text							,      **lowerCamelCase							,      padding=lowerCamelCase							,      return_tensors='tf'	)
        _UpperCamelCase  :							List[Any]	    =	self.model.generate(
            model_inputs.input_ids							,      attention_mask=model_inputs.attention_mask							,      num_beams=2							,      max_length=1_2_8	)
        _UpperCamelCase  :							List[Any]	    =	self.tokenizer.batch_decode(generated_ids.numpy()							,      skip_special_tokens=lowerCamelCase	)
        return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Optional[int]					     =			'mt'
 _lowerCamelCase    :						Any					     =			'en'
 _lowerCamelCase    :						Dict					     =			['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
 _lowerCamelCase    :						List[Any]					     =			['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']

 @unittest.skip('Skipping until #12647 is resolved.'	)
 @slow
 def        __snake_case       (      self							:		Optional[Any]	)			->				Optional[int]:
        self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Optional[Any]					     =			'en'
 _lowerCamelCase    :						List[str]					     =			'zh'
 _lowerCamelCase    :						str					     =			['My name is Wolfgang and I live in Berlin']
 _lowerCamelCase    :						Union[str, Any]					     =			['我叫沃尔夫冈 我住在柏林']

 @unittest.skip('Skipping until #12647 is resolved.'	)
 @slow
 def        __snake_case       (      self							:		Tuple	)			->				str:
        self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Optional[int]					     =			'en'
 _lowerCamelCase    :						Any					     =			'ROMANCE'
 _lowerCamelCase    :						str					     =			[
     '>>fr<< Don\'t spend so much time watching TV.',
     '>>pt<< Your message has been sent.',
     '>>es<< He\'s two years older than me.',
 ]
 _lowerCamelCase    :						List[Any]					     =			[
     'Ne passez pas autant de temps à regarder la télé.',
     'A sua mensagem foi enviada.',
     'Es dos años más viejo que yo.',
 ]

 @unittest.skip('Skipping until #12647 is resolved.'	)
 @slow
 def        __snake_case       (      self							:		str	)			->				Optional[int]:
        self._assert_generated_batch_equal_expected()

 @unittest.skip('Skipping until #12647 is resolved.'	)
 @slow
 def        __snake_case       (      self							:		Optional[Any]	)			->				Any:
        _UpperCamelCase  :							Tuple	    =	TranslationPipeline(self.model							,      self.tokenizer							,      framework='tf'	)
        _UpperCamelCase  :							Tuple	    =	pipeline(self.src_text	)
        self.assertEqual(self.expected_text							,      [x['translation_text'] for x in output]	)




import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput




@dataclass
class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[List[PIL.Image.Image], np.ndarray]







class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		int	)			->				Tuple:
        _UpperCamelCase  :							str	    =	CustomOutput(images=np.random.rand(1							,      3							,      4							,      4	)	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							str	    =	CustomOutput(images=[PIL.Image.new('RGB'							,      (4, 4)	)]	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)

 def        __snake_case       (      self							:		Any	)			->				Dict:
        # test output reinitialization with a `dict` for compatibility with `accelerate`
        _UpperCamelCase  :							Tuple	    =	CustomOutput({'images': np.random.rand(1							,      3							,      4							,      4	)}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							int	    =	CustomOutput({'images': [PIL.Image.new('RGB'							,      (4, 4)	)]}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)



