



'''simple docstring'''

import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

snake_case_			=     yaml.safe_load(
    """\
name: \"\"
allow_empty: false
allow_empty_text: true
subsections:
  - name: \"Dataset Card for X\" # First-level markdown heading
    allow_empty: false
    allow_empty_text: true
    subsections:
      - name: \"Table of Contents\"
        allow_empty: false
        allow_empty_text: false
        subsections: null
      - name: \"Dataset Description\"
        allow_empty: false
        allow_empty_text: false
        subsections:
          - name: \"Dataset Summary\"
            allow_empty: false
            allow_empty_text: false
            subsections: null
          - name: \"Supported Tasks and Leaderboards\"
            allow_empty: true
            allow_empty_text: true
            subsections: null
          - name: Languages
            allow_empty: false
            allow_empty_text: true
            subsections: null
"""
)


snake_case_			=     {
    """name""": """root""",
    """text""": """""",
    """is_empty_text""": True,
    """subsections""": [
        {
            """name""": """Dataset Card for My Dataset""",
            """text""": """""",
            """is_empty_text""": True,
            """subsections""": [
                {"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
                {
                    """name""": """Dataset Description""",
                    """text""": """Some text here.""",
                    """is_empty_text""": False,
                    """subsections""": [
                        {
                            """name""": """Dataset Summary""",
                            """text""": """Some text here.""",
                            """is_empty_text""": False,
                            """subsections""": [],
                        },
                        {
                            """name""": """Supported Tasks and Leaderboards""",
                            """text""": """""",
                            """is_empty_text""": True,
                            """subsections""": [],
                        },
                        {"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
                    ],
                },
            ],
        }
    ],
}


snake_case_			=     """\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""


snake_case_			=     """\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

snake_case_			=     {
    """name""": """root""",
    """text""": """""",
    """is_empty_text""": True,
    """subsections""": [
        {
            """name""": """Dataset Card for My Dataset""",
            """text""": """""",
            """is_empty_text""": True,
            """subsections""": [
                {"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
                {
                    """name""": """Dataset Description""",
                    """text""": """Some text here.""",
                    """is_empty_text""": False,
                    """subsections""": [
                        {
                            """name""": """Dataset Summary""",
                            """text""": """Some text here.""",
                            """is_empty_text""": False,
                            """subsections""": [
                                {
                                    """name""": """Extra Ignored Subsection""",
                                    """text""": """""",
                                    """is_empty_text""": True,
                                    """subsections""": [],
                                }
                            ],
                        },
                        {
                            """name""": """Supported Tasks and Leaderboards""",
                            """text""": """""",
                            """is_empty_text""": True,
                            """subsections""": [],
                        },
                        {"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
                    ],
                },
            ],
        }
    ],
}

snake_case_			=     """\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

snake_case_			=     (
    """The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."""
)

snake_case_			=     """\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

snake_case_			=     (
    """The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."""
)

snake_case_			=     """\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

snake_case_			=     """The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."""

snake_case_			=     """\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
snake_case_			=     """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."""


snake_case_			=     """\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
"""
snake_case_			=     """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found 'None'."""

snake_case_			=     """\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
"""

snake_case_			=     """The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."""


snake_case_			=     """\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
"""

snake_case_			=     """The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."""

snake_case_			=     """\
---
language:
- zh
- en
---

## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
snake_case_			=     """The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."""

snake_case_			=     """\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
"""

snake_case_			=     """The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."""

snake_case_			=     """\
---
language:
- zh
- en
---

# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

snake_case_			=     """The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."""

snake_case_			=     """"""

snake_case_			=     """The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."""

snake_case_			=     """\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

snake_case_			=     """The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."""






@pytest.mark.parametrize(
    '''readme_md, expected_dict'''    ,	[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]    ,	)
def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :str       ):
				assert ReadMe.from_string(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ).to_dict() == expected_dict






@pytest.mark.parametrize(
    '''readme_md, expected_error'''    ,	[
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]    ,	)
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :Any       ):
				with pytest.raises(_SCREAMING_SNAKE_CASE    ,	match=re.escape(expected_error.format(path='''root'''       )       )       ):
								SCREAMING_SNAKE_CASE  :						str      					= ReadMe.from_string(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								readme.validate()






@pytest.mark.parametrize(
    '''readme_md, expected_error'''    ,	[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]    ,	)
def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :int       ):
				with pytest.raises(_SCREAMING_SNAKE_CASE    ,	match=re.escape(expected_error.format(path='''root'''       )       )       ):
								ReadMe.from_string(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )






@pytest.mark.parametrize(
    '''readme_md,'''    ,	[
        (README_MULTIPLE_SAME_HEADING_1),
    ]    ,	)
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				ReadMe.from_string(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	suppress_parsing_errors=_SCREAMING_SNAKE_CASE       )






@pytest.mark.parametrize(
    '''readme_md, expected_dict'''    ,	[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]    ,	)
def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				with tempfile.TemporaryDirectory() as tmp_dir:
								SCREAMING_SNAKE_CASE  :						int      					= Path(_SCREAMING_SNAKE_CASE       ) / '''README.md'''
								with open(_SCREAMING_SNAKE_CASE    ,	'''w+'''       ) as readme_file:
												readme_file.write(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ReadMe.from_readme(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ).to_dict()
								assert out["name"] == path
								assert out["text"] == ""
								assert out["is_empty_text"]
								assert out["subsections"] == expected_dict["subsections"]






@pytest.mark.parametrize(
    '''readme_md, expected_error'''    ,	[
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]    ,	)
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :List[str]       ):
				with tempfile.TemporaryDirectory() as tmp_dir:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= Path(_SCREAMING_SNAKE_CASE       ) / '''README.md'''
								with open(_SCREAMING_SNAKE_CASE    ,	'''w+'''       ) as readme_file:
												readme_file.write(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= expected_error.format(path=_SCREAMING_SNAKE_CASE       )
								with pytest.raises(_SCREAMING_SNAKE_CASE    ,	match=re.escape(_SCREAMING_SNAKE_CASE       )       ):
												SCREAMING_SNAKE_CASE  :						Any      					= ReadMe.from_readme(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
												readme.validate()






@pytest.mark.parametrize(
    '''readme_md, expected_error'''    ,	[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]    ,	)
def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				with tempfile.TemporaryDirectory() as tmp_dir:
								SCREAMING_SNAKE_CASE  :						Tuple      					= Path(_SCREAMING_SNAKE_CASE       ) / '''README.md'''
								with open(_SCREAMING_SNAKE_CASE    ,	'''w+'''       ) as readme_file:
												readme_file.write(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						str      					= expected_error.format(path=_SCREAMING_SNAKE_CASE       )
								with pytest.raises(_SCREAMING_SNAKE_CASE    ,	match=re.escape(_SCREAMING_SNAKE_CASE       )       ):
												ReadMe.from_readme(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )






@pytest.mark.parametrize(
    '''readme_md,'''    ,	[
        (README_MULTIPLE_SAME_HEADING_1),
    ]    ,	)
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				with tempfile.TemporaryDirectory() as tmp_dir:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= Path(_SCREAMING_SNAKE_CASE       ) / '''README.md'''
								with open(_SCREAMING_SNAKE_CASE    ,	'''w+'''       ) as readme_file:
												readme_file.write(_SCREAMING_SNAKE_CASE       )
								ReadMe.from_readme(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	suppress_parsing_errors=_SCREAMING_SNAKE_CASE       )









'''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 	a__     (				_lowercase,			_lowercase	):


	@register_to_config
	def __init__(self : List[str],					__UpperCAmelCase : int = 128,					__UpperCAmelCase : int = 256,					__UpperCAmelCase : float = 2000.0,					__UpperCAmelCase : int = 768,					__UpperCAmelCase : int = 12,					__UpperCAmelCase : int = 12,					__UpperCAmelCase : int = 64,					__UpperCAmelCase : int = 2048,					__UpperCAmelCase : float = 0.1,					)     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()

					SCREAMING_SNAKE_CASE  :						int      					= nn.Sequential(
					    nn.Linear(__UpperCAmelCase,					d_model * 4,					bias=__UpperCAmelCase      ),					nn.SiLU(),					nn.Linear(d_model * 4,					d_model * 4,					bias=__UpperCAmelCase      ),					nn.SiLU(),					)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Embedding(__UpperCAmelCase,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= False

					SCREAMING_SNAKE_CASE  :						str      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Dropout(p=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= nn.ModuleList()
					for lyr_num in range(__UpperCAmelCase      ):
									# FiLM conditional T5 decoder
									SCREAMING_SNAKE_CASE  :						List[Any]      					= DecoderLayer(d_model=__UpperCAmelCase,					d_kv=__UpperCAmelCase,					num_heads=__UpperCAmelCase,					d_ff=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase      )
									self.decoders.append(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= TaLayerNorm(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Dropout(p=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.mul(query_input.unsqueeze(-1      ),					key_input.unsqueeze(-2      )      )
					return mask.unsqueeze(-3      )






	def 				lowercase__  (self : str,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

					# decoder_noise_time is in [0, 1), so rescale to expected timing range.
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.conditioning_emb(__UpperCAmelCase      ).unsqueeze(1      )

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

					SCREAMING_SNAKE_CASE  :						Tuple      					= 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.
					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.broadcast_to(
					    torch.arange(__UpperCAmelCase,					device=decoder_input_tokens.device      ),					(batch, seq_length),					)

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.position_encoding(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.continuous_inputs_projection(__UpperCAmelCase      )
					inputs += position_encodings
					SCREAMING_SNAKE_CASE  :						Dict      					= self.dropout(__UpperCAmelCase      )

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

					# Translate encoding masks to encoder-decoder masks.
					SCREAMING_SNAKE_CASE  :						Tuple      					= [(x, self.encoder_decoder_mask(__UpperCAmelCase,					__UpperCAmelCase      )) for x, y in encodings_and_masks]

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

					for lyr in self.decoders:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= lyr(
									    __UpperCAmelCase,					conditioning_emb=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					encoder_attention_mask=__UpperCAmelCase,					)[0]

					SCREAMING_SNAKE_CASE  :						str      					= self.decoder_norm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= self.post_dropout(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.spec_out(__UpperCAmelCase      )
					return spec_out



class 	a__     (				nn.Module	):


	def __init__(self : Union[str, Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Any=1e-6      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= 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 				lowercase__  (self : int,					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : Dict=None,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Optional[Any]=None,					__UpperCAmelCase : Dict=None,					)     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.layer[0](
					    __UpperCAmelCase,					conditioning_emb=__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					)

					if encoder_hidden_states is not None:
									SCREAMING_SNAKE_CASE  :						Any      					= torch.where(encoder_attention_mask > 0,					0,					-1e10      ).to(
									    encoder_hidden_states.dtype      )

									SCREAMING_SNAKE_CASE  :						Any      					= self.layer[1](
									    __UpperCAmelCase,					key_value_states=__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					)

					# Apply Film Conditional Feed Forward layer
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.layer[-1](__UpperCAmelCase,					__UpperCAmelCase      )

					return (hidden_states,)



class 	a__     (				nn.Module	):


	def __init__(self : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Dict      					= TaLayerNorm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= TaFiLMLayer(in_features=d_model * 4,					out_features=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Attention(query_dim=__UpperCAmelCase,					heads=__UpperCAmelCase,					dim_head=__UpperCAmelCase,					out_bias=__UpperCAmelCase,					scale_qk=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= nn.Dropout(__UpperCAmelCase      )






	def 				lowercase__  (self : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : List[str]=None,					)     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.layer_norm(__UpperCAmelCase      )

					if conditioning_emb is not None:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.FiLMLayer(__UpperCAmelCase,					__UpperCAmelCase      )

					# Self-attention block
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.attention(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= hidden_states + self.dropout(__UpperCAmelCase      )

					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : str,					__UpperCAmelCase : int,					__UpperCAmelCase : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int]      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Attention(query_dim=__UpperCAmelCase,					heads=__UpperCAmelCase,					dim_head=__UpperCAmelCase,					out_bias=__UpperCAmelCase,					scale_qk=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= TaLayerNorm(__UpperCAmelCase,					eps=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Dropout(__UpperCAmelCase      )






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Any=None,					__UpperCAmelCase : List[str]=None,					)     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.layer_norm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self.attention(
					    __UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					attention_mask=attention_mask.squeeze(1      ),					)
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= hidden_states + self.dropout(__UpperCAmelCase      )
					return layer_output



class 	a__     (				nn.Module	):


	def __init__(self : Optional[Any],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[int]      )     ->			List[str]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Dict      					= TaDenseGatedActDense(d_model=__UpperCAmelCase,					d_ff=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= TaFiLMLayer(in_features=d_model * 4,					out_features=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= TaLayerNorm(__UpperCAmelCase,					eps=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= nn.Dropout(__UpperCAmelCase      )






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Any,					__UpperCAmelCase : List[Any]=None      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.layer_norm(__UpperCAmelCase      )
					if conditioning_emb is not None:
									SCREAMING_SNAKE_CASE  :						Tuple      					= self.film(__UpperCAmelCase,					__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= self.DenseReluDense(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_states + self.dropout(__UpperCAmelCase      )
					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : Optional[Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : str,					__UpperCAmelCase : str      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Dropout(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= NewGELUActivation()






	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.act(self.wi_a(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.wi_a(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= hidden_gelu * hidden_linear
					SCREAMING_SNAKE_CASE  :						int      					= self.dropout(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.wo(__UpperCAmelCase      )
					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : Dict,					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any=1e-6      )     ->			Union[str, Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Parameter(torch.ones(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= eps






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Any      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= hidden_states.to(torch.floataa      ).pow(2      ).mean(-1,					keepdim=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_states * torch.rsqrt(variance + self.variance_epsilon      )

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

					return self.weight * hidden_states



class 	a__     (				nn.Module	):


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : torch.Tensor      )     ->			torch.Tensor:

					"""simple docstring"""

					return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.04_4715 * torch.pow(__UpperCAmelCase,					3.0      ))      ))



class 	a__     (				nn.Module	):


	def __init__(self : str,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[str]      )     ->			List[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Linear(__UpperCAmelCase,					out_features * 2,					bias=__UpperCAmelCase      )






	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Dict,					__UpperCAmelCase : int      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.scale_bias(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= torch.chunk(__UpperCAmelCase,					2,					-1      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= x * (1 + scale) + shift
					return x




