






"""simple docstring"""



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__A		:       Union[str, Any]  				=		logging.get_logger(__name__)



def 						lowercase    (				__snake_case				: Union[tf.Tensor, np.ndarray]	):
		if isinstance(__snake_case		,					np.ndarray	):
				return list(tensor.shape	)

		lowercase_ : Union[str, Any]							       =   tf.shape(__snake_case	)

		if tensor.shape == tf.TensorShape(__snake_case	):
				return dynamic

		lowercase_ : Optional[int]							       =   tensor.shape.as_list()

		return [dynamic[i] if s is None else s for i, s in enumerate(__snake_case	)]



def 						lowercase    (				__snake_case				: tf.Tensor		,					__snake_case				: Optional[int] = None		,					__snake_case				: Optional[str] = None	):
		return tf.nn.softmax(logits=logits + 1e-9		,					axis=__snake_case		,					name=__snake_case	)



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Union[str, Any]		,					__snake_case				: List[str]		,					__snake_case				: Union[str, Any]=1e-5		,					__snake_case				: Optional[int]=-1	):
		# This is a very simplified functional layernorm, designed to duplicate
		# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
		# models in Transformers.

		if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__snake_case		,					__snake_case	):
				raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''	)

		# Get mean and variance on the axis to be normalized
		lowercase_       ,					lowercase_ : Dict							       =   tf.nn.moments(__snake_case		,					axes=[axis]		,					keepdims=__snake_case	)

		if axis != -1:
				# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
				# on every dimension except axis
				lowercase_ : Optional[Any]							       =   [1] * inputs.shape.rank
				lowercase_ : List[str]							       =   shape_list(__snake_case	)[axis]
				lowercase_ : Any							       =   tf.reshape(__snake_case		,					__snake_case	)
				lowercase_ : Any							       =   tf.reshape(__snake_case		,					__snake_case	)

		# Compute layer normalization using the batch_normalization
		# function.
		lowercase_ : str							       =   tf.nn.batch_normalization(
		    __snake_case		,					__snake_case		,					__snake_case		,					offset=__snake_case		,					scale=__snake_case		,					variance_epsilon=__snake_case		,					)
		return outputs



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: int=0		,					__snake_case				: Dict=-1	):
		# Replicates the behavior of torch.flatten in TF

		# If end_dim or start_dim is negative, count them from the end
		if end_dim < 0:
				end_dim += input.shape.rank
		if start_dim < 0:
				start_dim += input.shape.rank

		if start_dim == end_dim:
				return input

		lowercase_ : Tuple							       =   tf.shape(__snake_case	)
		lowercase_ : Optional[int]							       =   tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]	)
		lowercase_ : Optional[Any]							       =   tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]		,					axis=0	)
		return tf.reshape(__snake_case		,					__snake_case	)



def 						lowercase    (				__snake_case				: tf.Tensor	):
		if not isinstance(__snake_case		,					tf.Tensor	):
				lowercase_ : Dict							       =   tf.convert_to_tensor(__snake_case	)  # Catches stray NumPy inputs
		if encoder_attention_mask.shape.rank == 3:
				lowercase_ : Union[str, Any]							       =   encoder_attention_mask[:, None, :, :]
		if encoder_attention_mask.shape.rank == 2:
				lowercase_ : List[Any]							       =   encoder_attention_mask[:, None, None, :]
		# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
		# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
		# /transformer/transformer_layers.py#L270
		# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
		# encoder_extended_attention_mask.transpose(-1, -2))
		lowercase_ : int							       =   (
		    tf.cast(1		,					encoder_attention_mask.dtype	) - encoder_extended_attention_mask
		) * encoder_extended_attention_mask.dtype.min

		return encoder_extended_attention_mask



def 						lowercase    (				__snake_case				: tf.Tensor		,					__snake_case				: int		,					__snake_case				: str = "input_ids"	):
		tf.debugging.assert_less(
		    __snake_case		,					tf.cast(__snake_case		,					dtype=tensor.dtype	)		,					message=(
		        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(__snake_case	)}) must be smaller than the embedding '''
		        F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
		    )		,					)



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Tuple		,					__snake_case				: Dict	):
		lowercase_ : int							       =   6_4_5_1_2
		# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
		# because in that case even chunking the array would not make the saving
		# possible.
		lowercase_ : Optional[int]							       =   [x for x in data if len(__snake_case	) > HDF5_OBJECT_HEADER_LIMIT]

		# Expecting this to never be true.
		if bad_attributes:
				raise RuntimeError(
				    '''The following attributes cannot be saved to HDF5 file because '''
				    F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
				    F'''bytes: {bad_attributes}'''	)

		lowercase_ : List[str]							       =   np.asarray(__snake_case	)

		lowercase_ : Union[str, Any]							       =   1
		lowercase_ : Dict							       =   np.array_split(__snake_case		,					__snake_case	)

		# This will never loop forever thanks to the test above.
		while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data	):
				num_chunks += 1
				lowercase_ : List[Any]							       =   np.array_split(__snake_case		,					__snake_case	)

		if num_chunks > 1:
				for chunk_id, chunk_data in enumerate(__snake_case	):
						lowercase_ : Any							       =   chunk_data
		else:
				lowercase_ : Tuple							       =   data



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: Union[str, Any]	):
		if name in group.attrs:
				lowercase_ : int							       =   [n.decode('''utf8'''	) if hasattr(__snake_case		,					'''decode'''	) else n for n in group.attrs[name]]
		else:
				lowercase_ : Any							       =   []
				lowercase_ : Union[str, Any]							       =   0
				while "%s%d" % (name, chunk_id) in group.attrs:
						data.extend(
						    [n.decode('''utf8'''	) if hasattr(__snake_case		,					'''decode'''	) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]	)
						chunk_id += 1
		return data






def 						lowercase    (				__snake_case				: Optional[Any]	):

		def _expand_single_ad_tensor(__snake_case				: Union[str, Any]	):
				if isinstance(__snake_case		,					tf.Tensor	) and t.shape.rank == 1:
						return tf.expand_dims(__snake_case		,					axis=-1	)
				return t

		return tf.nest.map_structure(_expand_single_ad_tensor		,					__snake_case	)









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],
                     )

