





import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
UpperCAmelCase_ 							=       pytest.mark.integration


UpperCAmelCase_ 							=       {"""comet"""}
UpperCAmelCase_ 							=       importlib.util.find_spec("""fairseq""") is not None

UpperCAmelCase_ 							=       {"""code_eval"""}
UpperCAmelCase_ 							=       os.name == """nt"""

UpperCAmelCase_ 							=       {"""bertscore""", """frugalscore""", """perplexity"""}
UpperCAmelCase_ 							=       importlib.util.find_spec("""transformers""") is not None





def   lowerCamelCase__       (       UpperCamelCase__     :    str  )     ->	str:



					'''simple docstring'''




					@wraps(UpperCamelCase__  )
					def wrapper(self     :    Optional[Any]    ,      UpperCamelCase__     :    Optional[Any]  ):
										if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
															self.skipTest('"test requires Fairseq"'  )
										else:
															test_case(self    ,      UpperCamelCase__  )

					return wrapper





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict  )     ->	Any:



					'''simple docstring'''




					@wraps(UpperCamelCase__  )
					def wrapper(self     :    Tuple    ,      UpperCamelCase__     :    List[Any]  ):
										if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
															self.skipTest('"test requires transformers"'  )
										else:
															test_case(self    ,      UpperCamelCase__  )

					return wrapper





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]  )     ->	List[str]:



					'''simple docstring'''




					@wraps(UpperCamelCase__  )
					def wrapper(self     :    str    ,      UpperCamelCase__     :    Dict  ):
										if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
															self.skipTest('"test not supported on Windows"'  )
										else:
															test_case(self    ,      UpperCamelCase__  )

					return wrapper





def   lowerCamelCase__       (       )     ->	Optional[int]:



					'''simple docstring'''




					_snake_case													=			[metric_dir.split(os.sep  )[-2] for metric_dir in glob.glob('./metrics/*/'  )]
					return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names()   )
@for_all_test_methods(
    _lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase   )
@local
class      UpperCamelCase_						(					parameterized.TestCase   ):
					lowerCAmelCase_							=		{}
					lowerCAmelCase_							=		None




					@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'					)
					@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'					)
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							str:
										_snake_case													=			'[...]'
										_snake_case													=			importlib.import_module(
										    datasets.load.metric_module_factory(os.path.join('metrics'			,  lowerCAmelCase_					)					).module_path					)
										_snake_case													=			datasets.load.import_main_class(metric_module.__name__			,  dataset=lowerCAmelCase_					)
										# check parameters
										_snake_case													=			inspect.signature(metric._compute					).parameters
										self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()					)					)  # no **kwargs
										# run doctest
										with self.patch_intensive_calls(lowerCAmelCase_			,  metric_module.__name__					):
															with self.use_local_metrics():
																				try:
																									_snake_case													=			doctest.testmod(lowerCAmelCase_			,  verbose=lowerCAmelCase_			,  raise_on_error=lowerCAmelCase_					)
																				except doctest.UnexpectedException as e:
																									raise e.exc_info[1]  # raise the exception that doctest caught
										self.assertEqual(results.failed			,  0					)
										self.assertGreater(results.attempted			,  1					)




					@slow
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Optional[int]:
										_snake_case													=			'[...]'
										_snake_case													=			importlib.import_module(
										    datasets.load.metric_module_factory(os.path.join('metrics'			,  lowerCAmelCase_					)					).module_path					)
										# run doctest
										with self.use_local_metrics():
															_snake_case													=			doctest.testmod(lowerCAmelCase_			,  verbose=lowerCAmelCase_			,  raise_on_error=lowerCAmelCase_					)
										self.assertEqual(results.failed			,  0					)
										self.assertGreater(results.attempted			,  1					)




					@contextmanager
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										if metric_name in self.INTENSIVE_CALLS_PATCHER:
															with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCAmelCase_					):
																				yield
										else:
															yield




					@contextmanager
					def 		lowerCAmelCase     (						self					)						->							Tuple:
										def load_local_metric(lowerCAmelCase_			,  *lowerCAmelCase_			,  **lowerCAmelCase_					):
															return load_metric(os.path.join('metrics'			,  lowerCAmelCase_					)			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)

										with patch('datasets.load_metric'					) as mock_load_metric:
															_snake_case													=			load_local_metric
															yield






					@classmethod
					def 		lowerCAmelCase     (						cls			,  lowerCAmelCase_					)						->							Optional[int]:
										def wrapper(lowerCAmelCase_					):
															_snake_case													=			contextmanager(lowerCAmelCase_					)
															_snake_case													=			patcher
															return patcher

										return wrapper





@LocalMetricTest.register_intensive_calls_patcher('bleurt'  )
def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]  )     ->	Optional[Any]:



					'''simple docstring'''




					import tensorflow.compat.va as tf
					from bleurt.score import Predictor

					tf.flags.DEFINE_string('sv'    ,      ''    ,      ''  )  # handle pytest cli flags
					class      UpperCamelCase_						(					_lowerCamelCase   ):




										def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							List[Any]:
															assert len(input_dict['input_ids']					) == 2
															return np.array([1.03, 1.04]					)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
					with patch('bleurt.score._create_predictor'  ) as mock_create_predictor:
										_snake_case													=			MockedPredictor()
										yield





@LocalMetricTest.register_intensive_calls_patcher('bertscore'  )
def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]  )     ->	Any:



					'''simple docstring'''




					import torch

					def bert_cos_score_idf(UpperCamelCase__     :    Union[str, Any]    ,      UpperCamelCase__     :    str    ,      *UpperCamelCase__     :    Dict    ,      **UpperCamelCase__     :    str  ):
										return torch.tensor([[1.0, 1.0, 1.0]] * len(UpperCamelCase__  )  )

					# mock get_model which is supposed to do download a bert model
					# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
					with patch('bert_score.scorer.get_model'  ), patch(
					    'bert_score.scorer.bert_cos_score_idf'  ) as mock_bert_cos_score_idf:
										_snake_case													=			bert_cos_score_idf
										yield





@LocalMetricTest.register_intensive_calls_patcher('comet'  )
def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]  )     ->	Optional[Any]:



					'''simple docstring'''




					def load_from_checkpoint(UpperCamelCase__     :    int  ):
										class      UpperCamelCase_						:




															def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							Tuple:
																				assert len(lowerCAmelCase_					) == 2
																				_snake_case													=			[0.19, 0.92]
																				return scores, sum(lowerCAmelCase_					) / len(lowerCAmelCase_					)





										return Model()

					# mock load_from_checkpoint which is supposed to do download a bert model
					# mock load_from_checkpoint which is supposed to do download a bert model
					with patch('comet.download_model'  ) as mock_download_model:
										_snake_case													=			None
										with patch('comet.load_from_checkpoint'  ) as mock_load_from_checkpoint:
															_snake_case													=			load_from_checkpoint
															yield





def   lowerCamelCase__       (       )     ->	Optional[Any]:



					'''simple docstring'''




					_snake_case													=			load_metric(os.path.join('metrics'    ,      'seqeval'  )  )
					_snake_case													=			'ERROR'
					_snake_case													=			F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
					with pytest.raises(UpperCamelCase__    ,      match=re.escape(UpperCamelCase__  )  ):
										metric.compute(predictions=[]    ,      references=[]    ,      scheme=UpperCamelCase__  )










import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase   ):




					@register_to_config
					def __init__(						self			,  lowerCAmelCase_ = 128			,  lowerCAmelCase_ = 256			,  lowerCAmelCase_ = 20_00.0			,  lowerCAmelCase_ = 768			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 64			,  lowerCAmelCase_ = 2048			,  lowerCAmelCase_ = 0.1			,  )						->							Union[str, Any]:
										super().__init__()

										_snake_case													=			nn.Sequential(
										    nn.Linear(lowerCAmelCase_			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  nn.Linear(d_model * 4			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  )

										_snake_case													=			nn.Embedding(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			False

										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)

										_snake_case													=			nn.ModuleList()
										for lyr_num in range(lowerCAmelCase_					):
															# FiLM conditional T5 decoder
															_snake_case													=			DecoderLayer(d_model=lowerCAmelCase_			,  d_kv=lowerCAmelCase_			,  num_heads=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)
															self.decoders.append(lowerCAmelCase_					)

										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			torch.mul(query_input.unsqueeze(-1					)			,  key_input.unsqueeze(-2					)					)
										return mask.unsqueeze(-3					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case							,       _snake_case							,       _snake_case													=			decoder_input_tokens.shape
										assert decoder_noise_time.shape == (batch,)

										# decoder_noise_time is in [0, 1), so rescale to expected timing range.
										_snake_case													=			get_timestep_embedding(
										    decoder_noise_time * self.config.max_decoder_noise_time			,  embedding_dim=self.config.d_model			,  max_period=self.config.max_decoder_noise_time			,  ).to(dtype=self.dtype					)

										_snake_case													=			self.conditioning_emb(lowerCAmelCase_					).unsqueeze(1					)

										assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

										_snake_case													=			decoder_input_tokens.shape[1]

										# If we want to use relative positions for audio context, we can just offset
										# this sequence by the length of encodings_and_masks.
										_snake_case													=			torch.broadcast_to(
										    torch.arange(lowerCAmelCase_			,  device=decoder_input_tokens.device					)			,  (batch, seq_length)			,  )

										_snake_case													=			self.position_encoding(lowerCAmelCase_					)

										_snake_case													=			self.continuous_inputs_projection(lowerCAmelCase_					)
										inputs += position_encodings
										_snake_case													=			self.dropout(lowerCAmelCase_					)

										# decoder: No padding present.
										_snake_case													=			torch.ones(
										    decoder_input_tokens.shape[:2]			,  device=decoder_input_tokens.device			,  dtype=inputs.dtype					)

										# Translate encoding masks to encoder-decoder masks.
										_snake_case													=			[(x, self.encoder_decoder_mask(lowerCAmelCase_			,  lowerCAmelCase_					)) for x, y in encodings_and_masks]

										# cross attend style: concat encodings
										_snake_case													=			torch.cat([x[0] for x in encodings_and_encdec_masks]			,  dim=1					)
										_snake_case													=			torch.cat([x[1] for x in encodings_and_encdec_masks]			,  dim=-1					)

										for lyr in self.decoders:
															_snake_case													=			lyr(
															    lowerCAmelCase_			,  conditioning_emb=lowerCAmelCase_			,  encoder_hidden_states=lowerCAmelCase_			,  encoder_attention_mask=lowerCAmelCase_			,  )[0]

										_snake_case													=			self.decoder_norm(lowerCAmelCase_					)
										_snake_case													=			self.post_dropout(lowerCAmelCase_					)

										_snake_case													=			self.spec_out(lowerCAmelCase_					)
										return spec_out





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							Tuple:
										super().__init__()
										_snake_case													=			nn.ModuleList()

										# cond self attention: layer 0
										self.layer.append(
										    TaLayerSelfAttentionCond(d_model=lowerCAmelCase_			,  d_kv=lowerCAmelCase_			,  num_heads=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)					)

										# cross attention: layer 1
										self.layer.append(
										    TaLayerCrossAttention(
										        d_model=lowerCAmelCase_			,  d_kv=lowerCAmelCase_			,  num_heads=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_			,  layer_norm_epsilon=lowerCAmelCase_			,  )					)

										# Film Cond MLP + dropout: last layer
										self.layer.append(
										    TaLayerFFCond(d_model=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_			,  layer_norm_epsilon=lowerCAmelCase_					)					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							Tuple:
										_snake_case													=			self.layer[0](
										    lowerCAmelCase_			,  conditioning_emb=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

										if encoder_hidden_states is not None:
															_snake_case													=			torch.where(encoder_attention_mask > 0			,  0			,  -1E10					).to(
															    encoder_hidden_states.dtype					)

															_snake_case													=			self.layer[1](
															    lowerCAmelCase_			,  key_value_states=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

										# Apply Film Conditional Feed Forward layer
										_snake_case													=			self.layer[-1](lowerCAmelCase_			,  lowerCAmelCase_					)

										return (hidden_states,)





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										super().__init__()
										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							str:
										# pre_self_attention_layer_norm
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)

										if conditioning_emb is not None:
															_snake_case													=			self.FiLMLayer(lowerCAmelCase_			,  lowerCAmelCase_					)

										# Self-attention block
										_snake_case													=			self.attention(lowerCAmelCase_					)

										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)

										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							str:
										super().__init__()
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							Dict:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										_snake_case													=			self.attention(
										    lowerCAmelCase_			,  encoder_hidden_states=lowerCAmelCase_			,  attention_mask=attention_mask.squeeze(1					)			,  )
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return layer_output





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							List[str]:
										super().__init__()
										_snake_case													=			TaDenseGatedActDense(d_model=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							Union[str, Any]:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										if conditioning_emb is not None:
															_snake_case													=			self.film(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			self.DenseReluDense(lowerCAmelCase_					)
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)
										_snake_case													=			NewGELUActivation()






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Any:
										_snake_case													=			self.act(self.wi_a(lowerCAmelCase_					)					)
										_snake_case													=			self.wi_a(lowerCAmelCase_					)
										_snake_case													=			hidden_gelu * hidden_linear
										_snake_case													=			self.dropout(lowerCAmelCase_					)

										_snake_case													=			self.wo(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							str:
										super().__init__()
										_snake_case													=			nn.Parameter(torch.ones(lowerCAmelCase_					)					)
										_snake_case													=			eps






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
										# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
										# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
										# half-precision inputs is done in fp32

										_snake_case													=			hidden_states.to(torch.floataa					).pow(2					).mean(-1			,  keepdim=lowerCAmelCase_					)
										_snake_case													=			hidden_states * torch.rsqrt(variance + self.variance_epsilon					)

										# convert into half-precision if necessary
										if self.weight.dtype in [torch.floataa, torch.bfloataa]:
															_snake_case													=			hidden_states.to(self.weight.dtype					)

										return self.weight * hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							torch.Tensor:
										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase_			,  3.0					))					))





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Any:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  out_features * 2			,  bias=lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case													=			self.scale_bias(lowerCAmelCase_					)
										_snake_case							,       _snake_case													=			torch.chunk(lowerCAmelCase_			,  2			,  -1					)
										_snake_case													=			x * (1 + scale) + shift
										return x



