"""simple docstring"""






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowerCAmelCase__  :  str						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  int						     =	{
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class 				snake_case       ( __lowerCamelCase				):




					"""simple docstring"""

					snake_case__				    = 'blenderbot-small'
					snake_case__				    = ['past_key_values']
					snake_case__				    = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







					def __init__(				self   :	Tuple    ,lowerCamelCase__   :	Dict=50_265    ,lowerCamelCase__   :	List[str]=512    ,lowerCamelCase__   :	Any=8    ,lowerCamelCase__   :	int=2_048    ,lowerCamelCase__   :	str=16    ,lowerCamelCase__   :	Tuple=8    ,lowerCamelCase__   :	Optional[int]=2_048    ,lowerCamelCase__   :	Any=16    ,lowerCamelCase__   :	List[str]=0.0    ,lowerCamelCase__   :	Any=0.0    ,lowerCamelCase__   :	Tuple=True    ,lowerCamelCase__   :	Dict=True    ,lowerCamelCase__   :	str="gelu"    ,lowerCamelCase__   :	List[str]=512    ,lowerCamelCase__   :	List[str]=0.1    ,lowerCamelCase__   :	Union[str, Any]=0.0    ,lowerCamelCase__   :	Dict=0.0    ,lowerCamelCase__   :	List[str]=0.0_2    ,lowerCamelCase__   :	Tuple=1    ,lowerCamelCase__   :	Tuple=False    ,lowerCamelCase__   :	int=0    ,lowerCamelCase__   :	List[Any]=1    ,lowerCamelCase__   :	List[str]=2    ,lowerCamelCase__   :	Union[str, Any]=2    ,**lowerCamelCase__   :	Tuple    ,):
									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							d_model
									UpperCAmelCase__             =							encoder_ffn_dim
									UpperCAmelCase__             =							encoder_layers
									UpperCAmelCase__             =							encoder_attention_heads
									UpperCAmelCase__             =							decoder_ffn_dim
									UpperCAmelCase__             =							decoder_layers
									UpperCAmelCase__             =							decoder_attention_heads
									UpperCAmelCase__             =							dropout
									UpperCAmelCase__             =							attention_dropout
									UpperCAmelCase__             =							activation_dropout
									UpperCAmelCase__             =							activation_function
									UpperCAmelCase__             =							init_std
									UpperCAmelCase__             =							encoder_layerdrop
									UpperCAmelCase__             =							decoder_layerdrop
									UpperCAmelCase__             =							use_cache
									UpperCAmelCase__             =							encoder_layers
									UpperCAmelCase__             =							scale_embedding  # scale factor will be sqrt(d_model) if True

									super().__init__(
									    pad_token_id=UpperCamelCase_    ,bos_token_id=UpperCamelCase_    ,eos_token_id=UpperCamelCase_    ,is_encoder_decoder=UpperCamelCase_    ,decoder_start_token_id=UpperCamelCase_    ,forced_eos_token_id=UpperCamelCase_    ,**UpperCamelCase_    ,)






class 				snake_case       ( __lowerCamelCase				):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase__             =							OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]			)

													if self.use_past:
																	UpperCAmelCase__             =							{0: 'batch'}
																	UpperCAmelCase__             =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																	UpperCAmelCase__             =							{0: 'batch', 1: 'decoder_sequence'}
																	UpperCAmelCase__             =							{0: 'batch', 1: 'decoder_sequence'}

													if self.use_past:
																	self.fill_with_past_key_values_(UpperCamelCase_    ,direction='inputs'			)
									elif self.task == "causal-lm":
													# TODO: figure this case out.
													UpperCAmelCase__             =							OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]			)
													if self.use_past:
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							self.num_layers
																	for i in range(UpperCamelCase_			):
																					UpperCAmelCase__             =							{0: 'batch', 2: 'past_sequence + sequence'}
																					UpperCAmelCase__             =							{0: 'batch', 2: 'past_sequence + sequence'}
									else:
													UpperCAmelCase__             =							OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
													        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
													    ]			)

									return common_inputs







					@property
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase__             =							super().outputs
									else:
													UpperCAmelCase__             =							super(UpperCamelCase_    ,self			).outputs
													if self.use_past:
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							self.num_layers
																	for i in range(UpperCamelCase_			):
																					UpperCAmelCase__             =							{0: 'batch', 2: 'past_sequence + sequence'}
																					UpperCAmelCase__             =							{0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	PreTrainedTokenizer    ,lowerCamelCase__   :	int = -1    ,lowerCamelCase__   :	int = -1    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	Optional[TensorType] = None    ,):
									UpperCAmelCase__             =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_			)

									# Generate decoder inputs
									UpperCAmelCase__             =							seq_length if not self.use_past else 1
									UpperCAmelCase__             =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_			)
									UpperCAmelCase__             =							{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
									UpperCAmelCase__             =							dict(**UpperCamelCase_    ,**UpperCamelCase_			)

									if self.use_past:
													if not is_torch_available():
																	raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
													else:
																	import torch
													UpperCAmelCase__      ,	UpperCAmelCase__             =							common_inputs['input_ids'].shape
													UpperCAmelCase__             =							common_inputs['decoder_input_ids'].shape[1]
													UpperCAmelCase__      ,	UpperCAmelCase__             =							self.num_attention_heads
													UpperCAmelCase__             =							(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													UpperCAmelCase__             =							decoder_seq_length + 3
													UpperCAmelCase__             =							(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													UpperCAmelCase__             =							torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(UpperCamelCase_    ,UpperCamelCase_			)]    ,dim=1			)

													UpperCAmelCase__             =							[]
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													UpperCAmelCase__      ,	UpperCAmelCase__             =							self.num_layers
													UpperCAmelCase__             =							min(UpperCamelCase_    ,UpperCamelCase_			)
													UpperCAmelCase__             =							max(UpperCamelCase_    ,UpperCamelCase_			) - min_num_layers
													UpperCAmelCase__             =							'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

													for _ in range(UpperCamelCase_			):
																	common_inputs["past_key_values"].append(
																	    (
																	        torch.zeros(UpperCamelCase_			),
																	        torch.zeros(UpperCamelCase_			),
																	        torch.zeros(UpperCamelCase_			),
																	        torch.zeros(UpperCamelCase_			),
																	    )			)
													# TODO: test this.
													UpperCAmelCase__             =							encoder_shape if remaining_side_name == 'encoder' else decoder_shape
													for _ in range(UpperCamelCase_    ,UpperCamelCase_			):
																	common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase_			), torch.zeros(UpperCamelCase_			))			)
									return common_inputs







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	PreTrainedTokenizer    ,lowerCamelCase__   :	int = -1    ,lowerCamelCase__   :	int = -1    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	Optional[TensorType] = None    ,):
									UpperCAmelCase__             =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_			)

									if self.use_past:
													if not is_torch_available():
																	raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
													else:
																	import torch
													UpperCAmelCase__      ,	UpperCAmelCase__             =							common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													UpperCAmelCase__             =							seqlen + 2
													UpperCAmelCase__      ,	UpperCAmelCase__             =							self.num_layers
													UpperCAmelCase__      ,	UpperCAmelCase__             =							self.num_attention_heads
													UpperCAmelCase__             =							(
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													UpperCAmelCase__             =							common_inputs['attention_mask'].dtype
													UpperCAmelCase__             =							torch.cat(
													    [common_inputs['attention_mask'], torch.ones(UpperCamelCase_    ,UpperCamelCase_    ,dtype=UpperCamelCase_			)]    ,dim=1			)
													UpperCAmelCase__             =							[
													    (torch.zeros(UpperCamelCase_			), torch.zeros(UpperCamelCase_			)) for _ in range(UpperCamelCase_			)
													]
									return common_inputs







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	PreTrainedTokenizer    ,lowerCamelCase__   :	int = -1    ,lowerCamelCase__   :	int = -1    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	Optional[TensorType] = None    ,):
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									UpperCAmelCase__             =							compute_effective_axis_dimension(
									    UpperCamelCase_    ,fixed_dimension=OnnxConfig.default_fixed_batch    ,num_token_to_add=0			)

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									UpperCAmelCase__             =							tokenizer.num_special_tokens_to_add(UpperCamelCase_			)
									UpperCAmelCase__             =							compute_effective_axis_dimension(
									    UpperCamelCase_    ,fixed_dimension=OnnxConfig.default_fixed_sequence    ,num_token_to_add=UpperCamelCase_			)

									# Generate dummy inputs according to compute batch and sequence
									UpperCAmelCase__             =							[' '.join([tokenizer.unk_token]			) * seq_length] * batch_size
									UpperCAmelCase__             =							dict(tokenizer(UpperCamelCase_    ,return_tensors=UpperCamelCase_			)			)
									return common_inputs







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	PreTrainedTokenizer    ,lowerCamelCase__   :	int = -1    ,lowerCamelCase__   :	int = -1    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	Optional[TensorType] = None    ,):
									if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase__             =							self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    UpperCamelCase_    ,batch_size=UpperCamelCase_    ,seq_length=UpperCamelCase_    ,is_pair=UpperCamelCase_    ,framework=UpperCamelCase_			)

									elif self.task == "causal-lm":
													UpperCAmelCase__             =							self._generate_dummy_inputs_for_causal_lm(
													    UpperCamelCase_    ,batch_size=UpperCamelCase_    ,seq_length=UpperCamelCase_    ,is_pair=UpperCamelCase_    ,framework=UpperCamelCase_			)
									else:
													UpperCAmelCase__             =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    UpperCamelCase_    ,batch_size=UpperCamelCase_    ,seq_length=UpperCamelCase_    ,is_pair=UpperCamelCase_    ,framework=UpperCamelCase_			)

									return common_inputs







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	List[str]    ,lowerCamelCase__   :	List[str]			):
									if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase__             =							super()._flatten_past_key_values_(UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_			)
									else:
													UpperCAmelCase__             =							super(UpperCamelCase_    ,self			)._flatten_past_key_values_(
													    UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_			)









import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


UpperCAmelCase_									=	TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('', '|', '|'),
    datarow=DataRow('', '|', '|'),
    padding=1,
    with_header_hide=None,
)


UpperCAmelCase_									=	[]
UpperCAmelCase_									=	[]

UpperCAmelCase_									=	{'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}

UpperCAmelCase_									=	[
    {
        'type': 'header',
        'text': {
            'type': 'plain_text',
            'text': f"""🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results""",
            'emoji': True,
        },
    }
]

UpperCAmelCase_									=	0
for log in Path().glob('*.log'):
       UpperCAmelCase_									=	0
       with open(log, 'r') as f:
              for line in f:
                     UpperCAmelCase_									=	json.loads(line)
                     if line.get('nodeid', '') != "":
                            UpperCAmelCase_									=	line['nodeid']
                            if line.get('duration', None) is not None:
                                   UpperCAmelCase_									=	f"""{line["duration"]:.4f}"""
                                   if line.get('outcome', '') == "failed":
                                          section_num_failed += 1
                                          failed.append([test, duration, log.name.split('_')[0]])
                                          total_num_failed += 1
       group_info.append([str(log), section_num_failed, failed])
       UpperCAmelCase_									=	[]
       log.unlink()

UpperCAmelCase_									=	''
UpperCAmelCase_									=	[]
if total_num_failed > 0:
       for name, num_failed, failed_tests in group_info:
              if num_failed > 0:
                     if num_failed == 1:
                            message += f"*{name[1:]}: {num_failed} failed test*\n"
                     else:
                            message += f"*{name[1:]}: {num_failed} failed tests*\n"
                     UpperCAmelCase_									=	[]
                     UpperCAmelCase_									=	{}
                     for test in failed_tests:
                            UpperCAmelCase_									=	test[0].split('::')
                            UpperCAmelCase_									=	data[0].split('/')[-1]
                            if data[0] not in filesafailed:
                                   UpperCAmelCase_									=	[data[1:]]
                            else:
                                   filesafailed[data[0]] += [data[1:]]
                            failed_table.append(data)

                     UpperCAmelCase_									=	[test[0] for test in failed_table]
                     UpperCAmelCase_									=	list(set(files))
                     # Count number of instances in failed_tests
                     UpperCAmelCase_									=	[]
                     for file in individual_files:
                            table.append([file, len(filesafailed[file])])

                     UpperCAmelCase_									=	tabulate(
                         table,
                         headers=['Test Location', 'Num Failed'],
                         tablefmt=hf_table_format,
                         stralign='right',
                     )
                     message += f"\n```\n{failed_table}\n```"
                     all_filesafailed.append(filesafailed)
       if len(message) > 3_000:
              UpperCAmelCase_									=	'Too many failed tests, please see the full report in the Action results.'
              UpperCAmelCase_									=	len(err) + 10
              UpperCAmelCase_									=	message[: 3_000 - offset] + f"""\n...\n```\n{err}"""
       print(f"""### {message}""")
else:
       UpperCAmelCase_									=	'No failed tests! 🤗'
       print(f"""## {message}""")
       payload.append(no_error_payload)

if os.environ.get('TEST_TYPE', '') != "":
       from slack_sdk import WebClient

       UpperCAmelCase_									=	WebClient(token=os.environ['SLACK_API_TOKEN'])
       if message != "No failed tests! 🤗":
              UpperCAmelCase_									=	{
                  'type': 'section',
                  'text': {
                      'type': 'mrkdwn',
                      'text': message,
                  },
              }
              payload.append(md_report)
              UpperCAmelCase_									=	{
                  'type': 'section',
                  'text': {
                      'type': 'mrkdwn',
                      'text': '*For more details:*',
                  },
                  'accessory': {
                      'type': 'button',
                      'text': {
                          'type': 'plain_text',
                          'text': 'Check Action results',
                          'emoji': True,
                      },
                      'url': f"""https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
                  },
              }
              payload.append(action_button)
              UpperCAmelCase_									=	{
                  'type': 'context',
                  'elements': [
                      {
                          'type': 'plain_text',
                          'text': f"""Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}""",
                      }
                  ],
              }
              payload.append(date_report)
       UpperCAmelCase_									=	client.chat_postMessage(channel='#accelerate-ci-daily', text=message, blocks=payload)
       UpperCAmelCase_									=	response.data['ts']
       for failed_file in all_filesafailed:
              for test_location, test_failures in failed_file.items():
                     # Keep only the first instance of the test name
                     UpperCAmelCase_									=	''
                     for i, row in enumerate(test_failures):
                            if row[0] != test_class:
                                   UpperCAmelCase_									=	row[0]
                            else:
                                   UpperCAmelCase_									=	''

                     UpperCAmelCase_									=	{
                         'type': 'section',
                         'text': {
                             'type': 'mrkdwn',
                             'text': f"""Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```""",
                         },
                     }

                     client.chat_postMessage(
                         channel='#accelerate-ci-daily',
                         thread_ts=ts,
                         blocks=[payload],
                     )

