





"""simple docstring"""


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        lowercase_			:
       snake_case__			:							List[Any]										=       MarianConfig
       snake_case__			:							Optional[Any]										=       {}
       snake_case__			:							str										=       """gelu"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=13				,   __SCREAMING_SNAKE_CASE=7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=99				,   __SCREAMING_SNAKE_CASE=32				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=4				,   __SCREAMING_SNAKE_CASE=37				,   __SCREAMING_SNAKE_CASE=0.1				,   __SCREAMING_SNAKE_CASE=0.1				,   __SCREAMING_SNAKE_CASE=20				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=1				,   __SCREAMING_SNAKE_CASE=0				,   ):






        '''simple docstring'''

        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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        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_marian_inputs_dict(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
        return config, inputs_dict

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			TFMarianModel(config=__SCREAMING_SNAKE_CASE       ).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(__SCREAMING_SNAKE_CASE				,   attention_mask=__SCREAMING_SNAKE_CASE				,   head_mask=__SCREAMING_SNAKE_CASE				,   use_cache=__SCREAMING_SNAKE_CASE       )

        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(__SCREAMING_SNAKE_CASE				,   attention_mask=__SCREAMING_SNAKE_CASE       )[0]
        snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   attention_mask=__SCREAMING_SNAKE_CASE				,   past_key_values=__SCREAMING_SNAKE_CASE       )[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(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   rtol=1e-3       )





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase=None		,       _UpperCAmelCase=None		,       _UpperCAmelCase=None		,       _UpperCAmelCase=None		,       _UpperCAmelCase=None		,       )      ->					Optional[Any]:

 '''simple docstring'''






 if attention_mask is None:
  snake_case__      							=			tf.cast(tf.math.not_equal(_UpperCAmelCase		,       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        lowercase_			(       __lowerCAmelCase			,__lowerCAmelCase			,unittest.TestCase							):
       snake_case__			:							Union[str, Any]										=       (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
       snake_case__			:							Tuple										=       (TFMarianMTModel,) if is_tf_available() else ()
       snake_case__			:							List[Any]										=       (
           {
               """conversational""": TFMarianMTModel,
               """feature-extraction""": TFMarianModel,
               """summarization""": TFMarianMTModel,
               """text2text-generation""": TFMarianMTModel,
               """translation""": TFMarianMTModel,
           }
           if is_tf_available()
           else {}
       )
       snake_case__			:							List[str]										=       True
       snake_case__			:							Optional[Any]										=       False
       snake_case__			:							Any										=       False

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			TFMarianModelTester(self       )
        snake_case__      							=			ConfigTester(self				,   config_class=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        self.config_tester.run_common_tests()

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE       )



@require_tf
class        lowercase_			(       unittest.TestCase							):
       snake_case__			:							Optional[int]										=       10_00  # show more chars for failing integration tests

       @classmethod
       def 						_SCREAMING_SNAKE_CASE(     cls       ):






        '''simple docstring'''

        snake_case__      							=			F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
        return cls

       @cached_property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return AutoTokenizer.from_pretrained(self.model_name       )

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self.tokenizer.eos_token_id

       @cached_property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        warnings.simplefilter("error"       )
        snake_case__      							=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
        assert isinstance(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			model.config
        self.assertListEqual(c.bad_words_ids				,   [[c.pad_token_id]]       )
        self.assertEqual(c.max_length				,   512       )
        self.assertEqual(c.decoder_start_token_id				,   c.pad_token_id       )
        return model

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.translate_src_text(**__SCREAMING_SNAKE_CASE       )
        self.assertListEqual(self.expected_text				,   __SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.tokenizer(self.src_text				,   **__SCREAMING_SNAKE_CASE				,   padding=__SCREAMING_SNAKE_CASE				,   return_tensors="tf"       )
        snake_case__      							=			self.model.generate(
            model_inputs.input_ids				,   attention_mask=model_inputs.attention_mask				,   num_beams=2				,   max_length=128       )
        snake_case__      							=			self.tokenizer.batch_decode(generated_ids.numpy()				,   skip_special_tokens=__SCREAMING_SNAKE_CASE       )
        return generated_words



@require_sentencepiece
@require_tokenizers
@require_tf
class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							int										=       """mt"""
       snake_case__			:							Dict										=       """en"""
       snake_case__			:							Dict										=       ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
       snake_case__			:							List[str]										=       ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]

       @unittest.skip("Skipping until #12647 is resolved."       )
       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        self._assert_generated_batch_equal_expected()



@require_sentencepiece
@require_tokenizers
@require_tf
class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[Any]										=       """en"""
       snake_case__			:							Any										=       """zh"""
       snake_case__			:							Optional[Any]										=       ["""My name is Wolfgang and I live in Berlin"""]
       snake_case__			:							Optional[Any]										=       ["""我叫沃尔夫冈 我住在柏林"""]

       @unittest.skip("Skipping until #12647 is resolved."       )
       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        self._assert_generated_batch_equal_expected()



@require_sentencepiece
@require_tokenizers
@require_tf
class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, Any]										=       """en"""
       snake_case__			:							str										=       """ROMANCE"""
       snake_case__			:							int										=       [
           """>>fr<< Don't spend so much time watching TV.""",
           """>>pt<< Your message has been sent.""",
           """>>es<< He's two years older than me.""",
       ]
       snake_case__			:							Optional[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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        self._assert_generated_batch_equal_expected()

       @unittest.skip("Skipping until #12647 is resolved."       )
       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			TranslationPipeline(self.model				,   self.tokenizer				,   framework="tf"       )
        snake_case__      							=			pipeline(self.src_text       )
        self.assertEqual(self.expected_text				,   [x["translation_text"] for x in output]       )








"""simple docstring"""


import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


a__   :       str   						=			logging.get_logger(__name__)



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Dict										=       """semantic"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=1_0000				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=768				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=0.7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1_0048				,   __SCREAMING_SNAKE_CASE=12_9595				,   __SCREAMING_SNAKE_CASE=12_9599				,   __SCREAMING_SNAKE_CASE=1_0000				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE=49.9				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(
            temperature=__SCREAMING_SNAKE_CASE				,   do_sample=__SCREAMING_SNAKE_CASE				,   eos_token_id=__SCREAMING_SNAKE_CASE				,   renormalize_logits=__SCREAMING_SNAKE_CASE				,   max_new_tokens=__SCREAMING_SNAKE_CASE				,   output_scores=__SCREAMING_SNAKE_CASE				,   return_dict_in_generate=__SCREAMING_SNAKE_CASE				,   output_hidden_states=__SCREAMING_SNAKE_CASE				,   output_attentions=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

        snake_case__      							=			text_encoding_offset
        snake_case__      							=			text_pad_token
        snake_case__      							=			eos_token_id
        snake_case__      							=			semantic_infer_token
        snake_case__      							=			semantic_vocab_size
        snake_case__      							=			max_input_semantic_length
        snake_case__      							=			semantic_rate_hz



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, Any]										=       """coarse_acoustics"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=0.7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1_2048				,   __SCREAMING_SNAKE_CASE=75				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=1_2050				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE = 630				,   __SCREAMING_SNAKE_CASE = 60				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(
            temperature=__SCREAMING_SNAKE_CASE				,   do_sample=__SCREAMING_SNAKE_CASE				,   renormalize_logits=__SCREAMING_SNAKE_CASE				,   output_scores=__SCREAMING_SNAKE_CASE				,   return_dict_in_generate=__SCREAMING_SNAKE_CASE				,   output_hidden_states=__SCREAMING_SNAKE_CASE				,   output_attentions=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

        snake_case__      							=			coarse_semantic_pad_token
        snake_case__      							=			coarse_rate_hz
        snake_case__      							=			n_coarse_codebooks
        snake_case__      							=			coarse_infer_token
        snake_case__      							=			max_coarse_input_length
        snake_case__      							=			max_coarse_history
        snake_case__      							=			sliding_window_len



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Union[str, Any]										=       """fine_acoustics"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=0.5				,   __SCREAMING_SNAKE_CASE=512				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=8				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(temperature=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			max_fine_history_length
        snake_case__      							=			max_fine_input_length
        snake_case__      							=			n_fine_codebooks



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[str]										=       """bark"""
       snake_case__			:							Dict										=       True

       def __init__(     self				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE=2_4000				,   __SCREAMING_SNAKE_CASE=1024				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        if semantic_config is None:
         snake_case__      							=			{}
         logger.info("semantic_config is None. initializing the semantic model with default values."       )

        if coarse_acoustics_config is None:
         snake_case__      							=			{}
         logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."       )

        if fine_acoustics_config is None:
         snake_case__      							=			{}
         logger.info("fine_acoustics_config is None. initializing the fine model with default values."       )

        snake_case__      							=			BarkSemanticGenerationConfig(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BarkCoarseGenerationConfig(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BarkFineGenerationConfig(**__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			sample_rate
        snake_case__      							=			codebook_size

       @classmethod
       def 						_SCREAMING_SNAKE_CASE(     cls				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        return cls(
            semantic_config=semantic_config.to_dict()				,   coarse_acoustics_config=coarse_acoustics_config.to_dict()				,   fine_acoustics_config=fine_acoustics_config.to_dict()				,   **__SCREAMING_SNAKE_CASE				,   )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			copy.deepcopy(self.__dict__       )

        snake_case__      							=			self.semantic_config.to_dict()
        snake_case__      							=			self.coarse_acoustics_config.to_dict()
        snake_case__      							=			self.fine_acoustics_config.to_dict()

        snake_case__      							=			self.__class__.model_type
        return output

