



'''simple docstring'''





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 				snake_case (UpperCamelCase		,      UpperCamelCase   ):



       @register_to_config
       def __init__(				self     ,UpperCAmelCase_ = 128     ,UpperCAmelCase_ = 256     ,UpperCAmelCase_ = 20_00.0     ,UpperCAmelCase_ = 768     ,UpperCAmelCase_ = 12     ,UpperCAmelCase_ = 12     ,UpperCAmelCase_ = 64     ,UpperCAmelCase_ = 2_048     ,UpperCAmelCase_ = 0.1     ,)  ->							List[Any]:
           super().__init__()

           lowercase__		       =						nn.Sequential(
               nn.Linear(UpperCAmelCase_     ,d_model * 4     ,bias=UpperCAmelCase_      )     ,nn.SiLU()     ,nn.Linear(d_model * 4     ,d_model * 4     ,bias=UpperCAmelCase_      )     ,nn.SiLU()     ,)

           lowercase__		       =						nn.Embedding(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						False

           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,UpperCAmelCase_     ,bias=UpperCAmelCase_      )

           lowercase__		       =						nn.Dropout(p=UpperCAmelCase_      )

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

           lowercase__		       =						TaLayerNorm(UpperCAmelCase_      )

           lowercase__		       =						nn.Dropout(p=UpperCAmelCase_      )
           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,UpperCAmelCase_     ,bias=UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						torch.mul(query_input.unsqueeze(-1      )     ,key_input.unsqueeze(-2      )      )
           return mask.unsqueeze(-3      )







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__       ,       lowercase__       ,       lowercase__		       =						decoder_input_tokens.shape
           assert decoder_noise_time.shape == (batch,)

           # decoder_noise_time is in [0, 1), so rescale to expected timing range.
           lowercase__		       =						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      )

           lowercase__		       =						self.conditioning_emb(UpperCAmelCase_      ).unsqueeze(1      )

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

           lowercase__		       =						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.
           lowercase__		       =						torch.broadcast_to(
               torch.arange(UpperCAmelCase_     ,device=decoder_input_tokens.device      )     ,(batch, seq_length)     ,)

           lowercase__		       =						self.position_encoding(UpperCAmelCase_      )

           lowercase__		       =						self.continuous_inputs_projection(UpperCAmelCase_      )
           inputs += position_encodings
           lowercase__		       =						self.dropout(UpperCAmelCase_      )

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

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

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

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

           lowercase__		       =						self.decoder_norm(UpperCAmelCase_      )
           lowercase__		       =						self.post_dropout(UpperCAmelCase_      )

           lowercase__		       =						self.spec_out(UpperCAmelCase_      )
           return spec_out





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_=1E-6      )  ->							Dict:
           super().__init__()
           lowercase__		       =						nn.ModuleList()

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

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

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







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,)  ->							Tuple:
           lowercase__		       =						self.layer[0](
               UpperCAmelCase_     ,conditioning_emb=UpperCAmelCase_     ,attention_mask=UpperCAmelCase_     ,)

           if encoder_hidden_states is not None:
               lowercase__		       =						torch.where(encoder_attention_mask > 0     ,0     ,-1E1_0      ).to(
                   encoder_hidden_states.dtype      )

               lowercase__		       =						self.layer[1](
                   UpperCAmelCase_     ,key_value_states=UpperCAmelCase_     ,attention_mask=UpperCAmelCase_     ,)

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

           return (hidden_states,)





class 				snake_case (nn.Module   ):



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







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,)  ->							List[str]:
           # pre_self_attention_layer_norm
           lowercase__		       =						self.layer_norm(UpperCAmelCase_      )

           if conditioning_emb is not None:
               lowercase__		       =						self.FiLMLayer(UpperCAmelCase_     ,UpperCAmelCase_      )

           # Self-attention block
           lowercase__		       =						self.attention(UpperCAmelCase_      )

           lowercase__		       =						hidden_states + self.dropout(UpperCAmelCase_      )

           return hidden_states





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Union[str, Any]:
           super().__init__()
           lowercase__		       =						Attention(query_dim=UpperCAmelCase_     ,heads=UpperCAmelCase_     ,dim_head=UpperCAmelCase_     ,out_bias=UpperCAmelCase_     ,scale_qk=UpperCAmelCase_      )
           lowercase__		       =						TaLayerNorm(UpperCAmelCase_     ,eps=UpperCAmelCase_      )
           lowercase__		       =						nn.Dropout(UpperCAmelCase_      )







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,)  ->							int:
           lowercase__		       =						self.layer_norm(UpperCAmelCase_      )
           lowercase__		       =						self.attention(
               UpperCAmelCase_     ,encoder_hidden_states=UpperCAmelCase_     ,attention_mask=attention_mask.squeeze(1      )     ,)
           lowercase__		       =						hidden_states + self.dropout(UpperCAmelCase_      )
           return layer_output





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							str:
           super().__init__()
           lowercase__		       =						TaDenseGatedActDense(d_model=UpperCAmelCase_     ,d_ff=UpperCAmelCase_     ,dropout_rate=UpperCAmelCase_      )
           lowercase__		       =						TaFiLMLayer(in_features=d_model * 4     ,out_features=UpperCAmelCase_      )
           lowercase__		       =						TaLayerNorm(UpperCAmelCase_     ,eps=UpperCAmelCase_      )
           lowercase__		       =						nn.Dropout(UpperCAmelCase_      )







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None      )  ->							int:
           lowercase__		       =						self.layer_norm(UpperCAmelCase_      )
           if conditioning_emb is not None:
               lowercase__		       =						self.film(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						self.DenseReluDense(UpperCAmelCase_      )
           lowercase__		       =						hidden_states + self.dropout(UpperCAmelCase_      )
           return hidden_states





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Any:
           super().__init__()
           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,UpperCAmelCase_     ,bias=UpperCAmelCase_      )
           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,UpperCAmelCase_     ,bias=UpperCAmelCase_      )
           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,UpperCAmelCase_     ,bias=UpperCAmelCase_      )
           lowercase__		       =						nn.Dropout(UpperCAmelCase_      )
           lowercase__		       =						NewGELUActivation()







       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Any:
           lowercase__		       =						self.act(self.wi_a(UpperCAmelCase_      )      )
           lowercase__		       =						self.wi_a(UpperCAmelCase_      )
           lowercase__		       =						hidden_gelu * hidden_linear
           lowercase__		       =						self.dropout(UpperCAmelCase_      )

           lowercase__		       =						self.wo(UpperCAmelCase_      )
           return hidden_states





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=1E-6      )  ->							Tuple:
           super().__init__()
           lowercase__		       =						nn.Parameter(torch.ones(UpperCAmelCase_      )      )
           lowercase__		       =						eps







       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Tuple:
           # 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

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

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

           return self.weight * hidden_states





class 				snake_case (nn.Module   ):



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





class 				snake_case (nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           super().__init__()
           lowercase__		       =						nn.Linear(UpperCAmelCase_     ,out_features * 2     ,bias=UpperCAmelCase_      )







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							int:
           lowercase__		       =						self.scale_bias(UpperCAmelCase_      )
           lowercase__       ,       lowercase__		       =						torch.chunk(UpperCAmelCase_     ,2     ,-1      )
           lowercase__		       =						x * (1 + scale) + shift
           return x









'''simple docstring'''





import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


SCREAMING_SNAKE_CASE__      					=     "http://www.mocksite.com/file1.txt"
SCREAMING_SNAKE_CASE__      					=     "\"text\": [\"foo\", \"foo\"]"
SCREAMING_SNAKE_CASE__      					=     "6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"


class 				snake_case :
       lowerCAmelCase__  :Union[str, Any]						   =					200
       lowerCAmelCase__  :Tuple						   =					{"Content-Length": "100"}
       lowerCAmelCase__  :Any						   =					{}



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							List[Any]:
           return [bytes(UpperCAmelCase_     ,"utf-8"      )]





def        lowerCamelCase  (				*_snake_case  :						int     ,**_snake_case  :						int       ):





    '''simple docstring'''


    return MockResponse()




@pytest.mark.parametrize("urls_type"     ,[str, list, dict]       )
def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						List[Any]     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


    import requests

    monkeypatch.setattr(_snake_case     ,"request"     ,_snake_case       )

    lowercase__		       =						URL
    if issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						url
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						[url]
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						{"train": url}
    lowercase__		       =						"dummy"
    lowercase__		       =						"downloads"
    lowercase__		       =						tmp_path
    lowercase__		       =						DownloadConfig(
        cache_dir=os.path.join(_snake_case     ,_snake_case       )     ,use_etag=_snake_case     ,)
    lowercase__		       =						DownloadManager(dataset_name=_snake_case     ,download_config=_snake_case       )
    lowercase__		       =						dl_manager.download(_snake_case       )
    lowercase__		       =						urls
    for downloaded_paths in [downloaded_paths]:
        if isinstance(_snake_case     ,_snake_case       ):
            lowercase__		       =						[downloaded_paths]
            lowercase__		       =						[urls]
        elif isinstance(_snake_case     ,_snake_case       ):
            assert "train" in downloaded_paths.keys()
            lowercase__		       =						downloaded_paths.values()
            lowercase__		       =						urls.values()
        assert downloaded_paths
        for downloaded_path, input_url in zip(_snake_case     ,_snake_case       ):
            assert downloaded_path == dl_manager.downloaded_paths[input_url]
            lowercase__		       =						Path(_snake_case       )
            lowercase__		       =						downloaded_path.parts
            assert parts[-1] == HASH
            assert parts[-2] == cache_subdir
            assert downloaded_path.exists()
            lowercase__		       =						downloaded_path.read_text()
            assert content == CONTENT
            lowercase__		       =						downloaded_path.with_suffix(".json"       )
            assert metadata_downloaded_path.exists()
            lowercase__		       =						json.loads(metadata_downloaded_path.read_text()       )
            assert metadata_content == {"url": URL, "etag": None}




@pytest.mark.parametrize("paths_type"     ,[str, list, dict]       )
def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						List[str]     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    lowercase__		       =						str(_snake_case       )
    if issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						filename
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						[filename]
    elif issubclass(_snake_case     ,_snake_case       ):
        lowercase__		       =						{"train": filename}
    lowercase__		       =						"dummy"
    lowercase__		       =						xz_file.parent
    lowercase__		       =						"extracted"
    lowercase__		       =						DownloadConfig(
        cache_dir=_snake_case     ,use_etag=_snake_case     ,)
    lowercase__		       =						DownloadManager(dataset_name=_snake_case     ,download_config=_snake_case       )
    lowercase__		       =						dl_manager.extract(_snake_case       )
    lowercase__		       =						paths
    for extracted_paths in [extracted_paths]:
        if isinstance(_snake_case     ,_snake_case       ):
            lowercase__		       =						[extracted_paths]
            lowercase__		       =						[paths]
        elif isinstance(_snake_case     ,_snake_case       ):
            assert "train" in extracted_paths.keys()
            lowercase__		       =						extracted_paths.values()
            lowercase__		       =						paths.values()
        assert extracted_paths
        for extracted_path, input_path in zip(_snake_case     ,_snake_case       ):
            assert extracted_path == dl_manager.extracted_paths[input_path]
            lowercase__		       =						Path(_snake_case       )
            lowercase__		       =						extracted_path.parts
            assert parts[-1] == hash_url_to_filename(_snake_case     ,etag=_snake_case       )
            assert parts[-2] == extracted_subdir
            assert extracted_path.exists()
            lowercase__		       =						extracted_path.read_text()
            lowercase__		       =						text_file.read_text()
            assert extracted_file_content == expected_file_content




def        lowerCamelCase  (				_snake_case  :						Tuple     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    assert path.endswith(".jsonl"       )
    for num_items, line in enumerate(_snake_case     ,start=1       ):
        lowercase__		       =						json.loads(line.decode("utf-8"       )       )
        assert item.keys() == {"col_1", "col_2", "col_3"}
    assert num_items == 4




@pytest.mark.parametrize("archive_jsonl"     ,["tar_jsonl_path", "zip_jsonl_path"]       )
def        lowerCamelCase  (				_snake_case  :						Any     ,_snake_case  :						Union[str, Any]       ):





    '''simple docstring'''


    lowercase__		       =						request.getfixturevalue(_snake_case       )
    lowercase__		       =						DownloadManager()
    for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_snake_case       )     ,start=1       ):
        _test_jsonl(_snake_case     ,_snake_case       )
    assert num_jsonl == 2




@pytest.mark.parametrize("archive_nested_jsonl"     ,["tar_nested_jsonl_path", "zip_nested_jsonl_path"]       )
def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						Dict       ):





    '''simple docstring'''


    lowercase__		       =						request.getfixturevalue(_snake_case       )
    lowercase__		       =						DownloadManager()
    for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_snake_case       )     ,start=1       ):
        for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_snake_case       )     ,start=1       ):
            _test_jsonl(_snake_case     ,_snake_case       )
    assert num_tar == 1
    assert num_jsonl == 2




def        lowerCamelCase  (				_snake_case  :						int       ):





    '''simple docstring'''


    lowercase__		       =						DownloadManager()
    for num_file, file in enumerate(dl_manager.iter_files(_snake_case       )     ,start=1       ):
        assert os.path.basename(_snake_case       ) == ("test.txt" if num_file == 1 else "train.txt")
    assert num_file == 2




