


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 SCREAMING_SNAKE_CASE :   List[str]							      =				"platform"

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_=None							,				lowerCamelCase_=None							,				lowerCamelCase_=None							,				lowerCamelCase_=None							,				lowerCamelCase_=None							,				lowerCamelCase_=None							,				)					->		Any:
    if attention_mask is None:
        _lowercase :							List[str]   	=	np.where(input_ids != config.pad_token_id							,				1							,				0				)
    if decoder_attention_mask is None:
        _lowercase :							Optional[int]   	=	np.where(decoder_input_ids != config.pad_token_id							,				1							,				0				)
    if head_mask is None:
        _lowercase :							List[Any]   	=	np.ones((config.encoder_layers, config.encoder_attention_heads)				)
    if decoder_head_mask is None:
        _lowercase :							Union[str, Any]   	=	np.ones((config.decoder_layers, config.decoder_attention_heads)				)
    if cross_attn_head_mask is None:
        _lowercase :							List[str]   	=	np.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": attention_mask,
    }






class _lowerCamelCase:
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=7,   lowerCamelCase=True,   lowerCamelCase=False,   lowerCamelCase=99,   lowerCamelCase=16,   lowerCamelCase=2,   lowerCamelCase=4,   lowerCamelCase=4,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=32,   lowerCamelCase=2,   lowerCamelCase=1,   lowerCamelCase=0,   lowerCamelCase=0.0_2,   ) ->     int:




           """simple docstring"""

           _lowercase :							Dict   	=	parent
           _lowercase :							str   	=	batch_size
           _lowercase :							int   	=	seq_length
           _lowercase :							Optional[Any]   	=	is_training
           _lowercase :							Dict   	=	use_labels
           _lowercase :							Any   	=	vocab_size
           _lowercase :							str   	=	hidden_size
           _lowercase :							Union[str, Any]   	=	num_hidden_layers
           _lowercase :							List[str]   	=	num_attention_heads
           _lowercase :							Optional[Any]   	=	intermediate_size
           _lowercase :							List[Any]   	=	hidden_act
           _lowercase :							Any   	=	hidden_dropout_prob
           _lowercase :							Optional[Any]   	=	attention_probs_dropout_prob
           _lowercase :							int   	=	max_position_embeddings
           _lowercase :							Union[str, Any]   	=	eos_token_id
           _lowercase :							Any   	=	pad_token_id
           _lowercase :							str   	=	bos_token_id
           _lowercase :							List[Any]   	=	initializer_range
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							int   	=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1],   self.vocab_size),   3,   self.vocab_size)
           _lowercase :							int   	=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),   dtype=np.intaa)),   -1)

           _lowercase :							List[str]   	=	shift_tokens_right(lowerCamelCase,   1,   2)

           _lowercase :							List[str]   	=	BlenderbotSmallConfig(
               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_id=self.eos_token_id,   bos_token_id=self.bos_token_id,   pad_token_id=self.pad_token_id,   initializer_range=self.initializer_range,   use_cache=lowerCamelCase,   )
           _lowercase :							Optional[int]   	=	prepare_blenderbot_inputs_dict(lowerCamelCase,   lowerCamelCase,   lowerCamelCase)
           return config, inputs_dict
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase	,       _lowercase :							Optional[Any]   	=	self.prepare_config_and_inputs()
           return config, inputs_dict
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Tuple:




           """simple docstring"""

           _lowercase :							List[Any]   	=	20
           _lowercase :							int   	=	model_class_name(lowerCamelCase)

           _lowercase :							Tuple   	=	model.encode(inputs_dict['input_ids'])

           _lowercase	,       _lowercase :							int   	=	(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           _lowercase :							List[str]   	=	model.init_cache(decoder_input_ids.shape[0],   lowerCamelCase,   lowerCamelCase)
           _lowercase :							List[Any]   	=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length),   dtype='i4')

           _lowercase :							List[Any]   	=	jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),   )
           _lowercase :							Tuple   	=	model.decode(
               decoder_input_ids[:, :-1],   lowerCamelCase,   decoder_attention_mask=lowerCamelCase,   past_key_values=lowerCamelCase,   decoder_position_ids=lowerCamelCase,   )

           _lowercase :							Optional[Any]   	=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],   dtype='i4')
           _lowercase :							List[Any]   	=	model.decode(
               decoder_input_ids[:, -1:],   lowerCamelCase,   decoder_attention_mask=lowerCamelCase,   past_key_values=outputs_cache.past_key_values,   decoder_position_ids=lowerCamelCase,   )

           _lowercase :							List[str]   	=	model.decode(lowerCamelCase,   lowerCamelCase)

           _lowercase :							Any   	=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
           self.parent.assertTrue(diff < 1E-3,   msg=F'''Max diff is {diff}''')




       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	20
           _lowercase :							Dict   	=	model_class_name(lowerCamelCase)

           _lowercase :							List[str]   	=	model.encode(inputs_dict['input_ids'])

           _lowercase	,       _lowercase :							Dict   	=	(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           _lowercase :							Optional[int]   	=	jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
               ],   axis=-1,   )

           _lowercase :							Any   	=	model.init_cache(decoder_input_ids.shape[0],   lowerCamelCase,   lowerCamelCase)
           _lowercase :							Optional[Any]   	=	jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),   )

           _lowercase :							Dict   	=	model.decode(
               decoder_input_ids[:, :-1],   lowerCamelCase,   decoder_attention_mask=lowerCamelCase,   past_key_values=lowerCamelCase,   decoder_position_ids=lowerCamelCase,   )
           _lowercase :							List[str]   	=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],   dtype='i4')
           _lowercase :							Dict   	=	model.decode(
               decoder_input_ids[:, -1:],   lowerCamelCase,   past_key_values=outputs_cache.past_key_values,   decoder_attention_mask=lowerCamelCase,   decoder_position_ids=lowerCamelCase,   )

           _lowercase :							Optional[Any]   	=	model.decode(lowerCamelCase,   lowerCamelCase,   decoder_attention_mask=lowerCamelCase)

           _lowercase :							List[str]   	=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
           self.parent.assertTrue(diff < 1E-3,   msg=F'''Max diff is {diff}''')






@require_flax
class _lowerCamelCase(		unittest.TestCase     ):
       lowercase_				:			Optional[Any]									=    99
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							str   	=	np.array(
               [
                   [71, 82, 18, 33, 46, 91, 2],
                   [68, 34, 26, 58, 30, 82, 2],
                   [5, 97, 17, 39, 94, 40, 2],
                   [76, 83, 94, 25, 70, 78, 2],
                   [87, 59, 41, 35, 48, 66, 2],
                   [55, 13, 16, 58, 5, 2, 1],  # note padding
                   [64, 27, 31, 51, 12, 75, 2],
                   [52, 64, 86, 17, 83, 39, 2],
                   [48, 61, 9, 24, 71, 82, 2],
                   [26, 1, 60, 48, 22, 13, 2],
                   [21, 5, 62, 28, 14, 76, 2],
                   [45, 98, 37, 86, 59, 48, 2],
                   [70, 70, 50, 9, 28, 0, 2],
               ],   dtype=np.intaa,   )

           _lowercase :							int   	=	input_ids.shape[0]
           _lowercase :							Union[str, Any]   	=	BlenderbotSmallConfig(
               vocab_size=self.vocab_size,   d_model=24,   encoder_layers=2,   decoder_layers=2,   encoder_attention_heads=2,   decoder_attention_heads=2,   encoder_ffn_dim=32,   decoder_ffn_dim=32,   max_position_embeddings=48,   eos_token_id=2,   pad_token_id=1,   bos_token_id=0,   )
           return config, input_ids, batch_size
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase	,       _lowercase	,       _lowercase :							Optional[Any]   	=	self._get_config_and_data()
           _lowercase :							str   	=	FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase)
           _lowercase :							List[Any]   	=	lm_model(input_ids=lowerCamelCase)
           _lowercase :							Tuple   	=	(batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['logits'].shape,   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Any   	=	BlenderbotSmallConfig(
               vocab_size=self.vocab_size,   d_model=14,   encoder_layers=2,   decoder_layers=2,   encoder_attention_heads=2,   decoder_attention_heads=2,   encoder_ffn_dim=8,   decoder_ffn_dim=8,   max_position_embeddings=48,   )
           _lowercase :							Optional[Any]   	=	FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase)
           _lowercase :							Union[str, Any]   	=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],   dtype=np.intaa)
           _lowercase :							List[str]   	=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],   dtype=np.intaa)
           _lowercase :							Optional[int]   	=	lm_model(input_ids=lowerCamelCase,   decoder_input_ids=lowerCamelCase)
           _lowercase :							int   	=	(*summary.shape, config.vocab_size)
           self.assertEqual(outputs['logits'].shape,   lowerCamelCase)




       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Tuple   	=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],   dtype=np.intaa)
           _lowercase :							Optional[int]   	=	shift_tokens_right(lowerCamelCase,   1,   2)
           _lowercase :							Any   	=	np.equal(lowerCamelCase,   1).astype(np.floataa).sum()
           _lowercase :							Tuple   	=	np.equal(lowerCamelCase,   1).astype(np.floataa).sum()
           self.assertEqual(shifted.shape,   input_ids.shape)
           self.assertEqual(lowerCamelCase,   n_pad_before - 1)
           self.assertTrue(np.equal(shifted[:, 0],   2).all())






@require_flax
class _lowerCamelCase(		_a,				unittest.TestCase,				_a     ):
       lowercase_				:			Union[str, Any]									=    True
       lowercase_				:			str									=    (
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       lowercase_				:			List[str]									=    (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
       def     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	FlaxBlenderbotSmallModelTester(self)
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase	,       _lowercase :							Any   	=	self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCamelCase,   lowerCamelCase,   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase	,       _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase,   lowerCamelCase,   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase	,       _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                   _lowercase :							str   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase)
                   _lowercase :							List[str]   	=	model_class(lowerCamelCase)

                   @jax.jit
                   def encode_jitted(lowerCamelCase,   lowerCamelCase=None,   **lowerCamelCase):
                       return model.encode(input_ids=lowerCamelCase,   attention_mask=lowerCamelCase)

                   with self.subTest('JIT Enabled'):
                       _lowercase :							List[str]   	=	encode_jitted(**lowerCamelCase).to_tuple()

                   with self.subTest('JIT Disabled'):
                       with jax.disable_jit():
                           _lowercase :							Dict   	=	encode_jitted(**lowerCamelCase).to_tuple()

                   self.assertEqual(len(lowerCamelCase),   len(lowerCamelCase))
                   for jitted_output, output in zip(lowerCamelCase,   lowerCamelCase):
                       self.assertEqual(jitted_output.shape,   output.shape)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase	,       _lowercase :							str   	=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                   _lowercase :							Tuple   	=	model_class(lowerCamelCase)
                   _lowercase :							Union[str, Any]   	=	model.encode(inputs_dict['input_ids'],   inputs_dict['attention_mask'])

                   _lowercase :							List[Any]   	=	{
                       'decoder_input_ids': inputs_dict['decoder_input_ids'],
                       'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                       'encoder_outputs': encoder_outputs,
                   }

                   @jax.jit
                   def decode_jitted(lowerCamelCase,   lowerCamelCase,   lowerCamelCase):
                       return model.decode(
                           decoder_input_ids=lowerCamelCase,   decoder_attention_mask=lowerCamelCase,   encoder_outputs=lowerCamelCase,   )

                   with self.subTest('JIT Enabled'):
                       _lowercase :							List[str]   	=	decode_jitted(**lowerCamelCase).to_tuple()

                   with self.subTest('JIT Disabled'):
                       with jax.disable_jit():
                           _lowercase :							Optional[Any]   	=	decode_jitted(**lowerCamelCase).to_tuple()

                   self.assertEqual(len(lowerCamelCase),   len(lowerCamelCase))
                   for jitted_output, output in zip(lowerCamelCase,   lowerCamelCase):
                       self.assertEqual(jitted_output.shape,   output.shape)




       @slow
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           for model_class_name in self.all_model_classes:
               _lowercase :							Union[str, Any]   	=	model_class_name.from_pretrained('facebook/blenderbot_small-90M')
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               _lowercase :							Tuple   	=	np.ones((1, 1)) * model.config.eos_token_id
               _lowercase :							Optional[int]   	=	model(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)






from jiwer import compute_measures

import datasets


SCREAMING_SNAKE_CASE :   Tuple							      =				"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

SCREAMING_SNAKE_CASE :   Any							      =				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION     )
class _lowerCamelCase(		datasets.Metric     ):
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           return datasets.MetricInfo(
               description=_DESCRIPTION,   citation=_CITATION,   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
                   {
                       'predictions': datasets.Value('string',   id='sequence'),
                       'references': datasets.Value('string',   id='sequence'),
                   }),   codebase_urls=['https://github.com/jitsi/jiwer/'],   reference_urls=[
                   'https://en.wikipedia.org/wiki/Word_error_rate',
               ],   )




       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=False) ->     Optional[Any]:




           """simple docstring"""

           if concatenate_texts:
               return compute_measures(lowerCamelCase,   lowerCamelCase)["wer"]
           else:
               _lowercase :							Dict   	=	0
               _lowercase :							Optional[int]   	=	0
               for prediction, reference in zip(lowerCamelCase,   lowerCamelCase):
                   _lowercase :							List[Any]   	=	compute_measures(lowerCamelCase,   lowerCamelCase)
                   incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                   total += measures["substitutions"] + measures["deletions"] + measures["hits"]
               return incorrect / total


